Jijenge Academy

I am an ex-JPM Investment Banker who moved from NYC to Nairobi to use my skills to impact social change. I started at an investment advisor, consulting and raising money for East African SME's and startups. Afterwards, I moved to an ed-tech startup as Growth Manager where I got implementation experience and refined the social business model for Pan-African expansion. 

Over 3 years, Jijenge developed organically from my volunteer work at a Nairobi orphanage to stop high school graduated orphans from returning to the slums from which they were rescued with no job prospects or support system.

My skills include agile project management, data-driven decision making, and finance. I have a proven entrepreneurial track record, and the fear tolerance of a honey badger. My interests include achieving operational excellence, developing sustainable social business models, and bridging intercultural gaps for product-market fit success. 

We equip vulnerable workers with the tools to break the cycle of poverty by teaching them a monetizable, digital skill to achieve financial security. With a 90% job placement rate and 4x average income increase, our program trains low-skilled workers with no computer literacy to become advanced data labellers in just two months.

We corner an untouched market gap in Africa: Enabling base-of-the-pyramid distributed workforces. We do this by 1) building cyber workstations to sustainably facilitate cheap access to high-specification laptops and high-speed internet, and 2) equipping third parties to deliver our training through social franchising. 

If scaled globally, our infrastructure can permanently replace dangerous manual labor work with data annotation work for the poorest people in the world. Doing that would catapult low-skilled workers into the digital economy, formal sector employment, and start them on the path of bridging the huge gaps created by social inequality in emerging markets.

Kenya has 10 million youth, 65% are unemployed. Most underserved youth earn through informal employment of <$50/mo in dangerous casual labor jobs such as tea plantations or slaughterhouses. Most young women in the slums are at risk of sexual harassment or early marriage.

Low skilled workers have no tools to achieve financial security, better education, or secure dignified work. This problem is compounded by lack of access to finance, and the lure of illegal activities to provide for basic needs like food. 

We believe this magnitude of unemployment and lack of education can only be solved by leapfrogging traditional education and career paths, and directly training the most underserved youth in a monetizable, digital skillset. That brings us to data labeling, which we believe is the most time and cost-effective digital skillset for low-skilled workers to achieve financial security.

Data labeling is the main bottleneck to the growth of machine learning, and its market is expected to triple to $5 billion by 2023. Kenyan data labeling companies have emerged as a result, however they haven't been able to run distributed workforces. Due to infrastructure challenges in East Africa, they have to run in-person managed workforces with limited job openings.

Our venture features three components that work together to help vulnerable workers break the cycle of poverty they've been born into.

1) Our training program takes workers from no computer literacy to advanced data labeling skills in 2 months. After 3 years of iteration, it is tailor made for those from the poorest backgrounds to succeed. Our community engagement strategy to acquire the most ambitious workers, anticipate their needs, and help them succeed is a key part of our product offering. The curriculum also includes general employability skills, teamwork skills, and sex education for girls.  

2) Our low income cyber workstation infrastructure is desperately needed to enable distributed workers in Africa. We charge workers a weekly access fee, which allows these workstations to pay themselves back after 12 months. After that, they become remain profitable for their remaining 2.5 year lifespan. 

3) Our international data labeling employment partners supply online data annotation work. Thus, workers can begin earning directly after the training program. Our B2B product is building affordable, distributed data labeling workforces for international companies and allowing them to take it in-house or keep paying us to manage it. 

We formed this venture (and our team of co-founders) by listening to the needs of high school graduated orphans from the slums. We used their feedback to form a sustainable solution for them to break the cycle of poverty in a lasting way. We came to a niche that was such a value add to their lives, that our dropout rate is non-existent.

By teaching computer literacy and facilitating job access, workers quadruple their income and access online resources to better their lives. After the program, students use their income to attend school part-time, move their families out of the slums, or sponsor their siblings to attend school. This creates a permanent and sustainable shift for vulnerable youth to help themselves, without external support. The best evidence of the life-changing impact we've achieved is in the autobiographies and income increases of our graduates on our website.

The success of our trainings depend on a deep understanding of the challenges vulnerable workers face, and the mobilisation of surrounding communities to enable their success. Our operations managers dual-function as community champions who familiarize themselves with community leaders and the most ambitious, needy youth of communities in geographic proximity to our workstations.

Our target customers are high school graduates from Kenya's most dangerous slums or faraway pastoralist communities who have never touched a computer before. Normally students in this socio-economic class are left behind, with next to no opportunities to break the cycle of poverty since their skills only allow them to work at dangerous, casual labor jobs like slaughterhouses or tea and rice plantations. 

We elevate opportunities for these students by teaching them a digital, monetizable skillset and building the infrastructure for them to use it to earn income. With financial security, they can access many opportunities like higher education.

I was volunteering at an orphanage in Kiambu, when my Kenyan co-founder (who worked at the orphanage at that time) and I tried to put a stop to a serious injustice: When students turned 18, they were kicked out of the orphanage with no support or skills, where they soon after returned to the slums or countryside from which they were rescued as children. It was clear they had no hope of breaking the cycle of poverty they were born into. We started offering general employability and computer literacy training, only to find that jobs were scarce and tailoring a training to available jobs was too resource intensive and could never be scalable. We discovered that BPO companies and platforms employed thousands of students annually, and the nature of BPO work allowed our students to succeed in roles filled by college graduates, despite not having skills such as full mastery of English. From there, armed with ambition, they can achieve their dreams by themselves. Armed with a love of helping others, they can lift their peers, family and friends out of poverty.

My inspiration to equip students with the tools to realise their ambition comes from my grandmother, an orphan who immigrated from a remote Greek mountain village alone when she was only 16 years old. She worked as a maid, learned to sew, and slowly saved to bring over her whole family to America and eventually own a bridal store. I believe that the most vulnerable youth should at the very least be given one opportunity or skill which they can use to pave their own road and achieve their dreams. 

I have only been living in Kenya for 3 years, but I feel it is my home. My first week in Kenya, I went to volunteer at an orphanage. After that, I kept visiting, and taught computer literacy to students by myself 3 nights per week after my full time job. I recognized myself in the high school graduates of the orphanage, and loved educating them and helping them jump through hoops to be considered for the same opportunities as their privileged, college-educated peers. I felt my passion was to get these students great jobs so they can earn and permanently help themselves. 

I believe my entrepreneurship, intercultural communication skills, and proven passion for listening to the needs of vulnerable youth well-position me to solve this problem. I started my career in Investment Banking at JP Morgan in New York. As I learned more about impact investing, I realised I wanted to use my skills to make the world a better place. That is when I moved to I-DEV International, an investment advisory shop in Kenya that consults and raises seed and Series A rounds for East African SME's and startups. There I developed my leadership and management skills, and my familiarity with the East African social enterprise and startup ecosystem. For example I created and taught a full week accelerator bootcamp for iHub to teach investor readiness, modelling, & growth strategy to 30 Pan-African startups, as well as independently ideated and implemented a successful 200-person young professionals in finance conference (and fundraised $16,000 from other firms to sponsor it).

Afterwards, I moved to an ed-tech startup as Growth Manager to gain experience in creating product-market fit success and implementing a sustainable business model for a social enterprise. At EIDU I managed the field team, standardised quality control processes, raised usability issues, prepped the startup for fundraising, discovered and solved data integrity issues, and took over as Sales and Marketing Manager to test various sales initiatives. With these skills, I want to build a sustainable pathway for Kenya's most vulnerable youth to participate in the fast growing international data labeling sector. 

In the early days of Jijenge, we exclusively worked with high school graduates at a Nairobi orphanage. After awhile, we realized the orphanage was very corrupt and mis-using funds designated for laptops. When we found out, we stopped sending any money to them directly. That started frightening issues.

The orphanage staff began intimidating the students and threatening us. They verbally abused them, withheld food portions, and even harassed them at work to get them fired from the data labeling jobs we got them. The woman who ran the orphanage even had someone stalk my friend home. 

Instead of giving in to threats and starting fresh with another group, my team and I organised an intervention. We brought together government officials, donors, and some orphanage staff to help us coordinate the exit of the 12 trainees from the orphanage. Thanks to us they escaped with all their belongings (which were usually confiscated), and we provided them food and housing until we got them jobs so they could live independently. 

By staying committed to helping our trainees succeed despite scary and difficult-to-understand hurdles, Jijenge's foundation was built on our first class of graduates who mentor and help all our future cohorts. 

I attempt to lead change in my personal and professional life whenever I can. Real change starts with daily action. That's why I spent a weekend hustling the system to get Patrick into boarding school. 

We met in downtown Nairobi one night. I could tell he was too smart to be sleeping on the street and sniffing glue at 12 years old. Luckily the Headmaster of the high-end school agreed too after her initial rejection on the basis that he was a "street-urchin". 

I'd never sponsored a student before, but in that moment I decided to do my part to make my home a better place, regardless of the risks. We drove hours away, dodged regulatory requirements, shopped for school supplies, and arranged medical attention until he was tucked into bed only two days after we met.

Similar to why I started Jijenge Academy, if I have the skills to impact change then it's my responsibility to. I understand that changing Patrick's life won't have as large an impact as a program like Jijenge, but being a leader means affecting change in your personal sphere and inspiring others to do the same in the face of overwhelming structural inequality.

We are the first to do what was previously unworkable: Quadruple the income of the world's poorest, low skilled workers in 2 months. Currently, there is no solution that 1) enables distributed data labeling workforces in Africa or 2) offers affordable high-specification laptop access and high-speed wifi access to low income workers.  

Our closest competitors are Samasource and Digital Divide Data. They manage in-person workers who commute and solicit for data labeling contracts to staff their employees. Both of these companies must accept workers that already have a strong foundation in computer literacy (which generates less impact) and run costly in-person managed workforces since 1) their work streams are not compatible with distributed work and 2) they have no affordable solution to facilitate worker's access to high speed wifi or high specification laptops. When they look upmarket for workers that already have these requirements, there is a salary mismatch with middle class workers which leads to high employee turnover and low motivation.

What is innovative about our revenue model is that we are not competing with data labeling shops, we build the infrastructure to enable them to run distributed workforces and reduce their costs. Since we derive our revenues from fees the workers pay to access our infrastructure, we have a cost-affordable edge over every other consultant or BPO workforce builder because we can undercut their pricing because of our secondary revenue stream. 

Our venture’s activities can be grouped into two areas. The first is a 2-month training program which includes computer literacy training, general employability training, as well as the development of advanced data labeling skills. 

The output of the computer literacy program includes access to beneficial knowledge and online programs. By teaching students advanced web browsing, we ensure they understand they can access unlimited information and e-learning portals to upskill themselves. An output created by the general employability training is career progression since we teach how to find jobs online, how to apply, how to professionally communicate, and structure how they think about job progression. Proof of this includes a number of our graduates who have moved into higher paying jobs or shortlisted for jobs with salaries 3x theirs. An output from our data labeling skills training is that workers develop a monetizable skillset alongside other job skills such as attention to detail and problem solving.

The outcomes of training vulnerable youth in a highly monetizable, tech skillset include decreasing the youth unemployment rate in emerging market countries and a higher participation rate of low skilled workers in the digital economy. The outcomes of vulnerable youth having access to beneficial knowledge and online programs are better health practices, better education, and lower rates of teen pregnancy (since young women can access information, products, and assistance online).

The second group of activities connect workers with data labeling jobs. They begin tasking on real work near the end of the training to ensure they're earning by the time they graduate. This creates two outputs: 1) workers have formal employment with promotion opportunities down the line and 2) workers graduate our program with a 4x income earnings increase.

The outcome of this income increase is financial security. After quadrupling earnings to ~$200 per month, they can afford their own housing outside the slums, basic needs, and are more resistant to financial shocks so they can continue saving to achieve their goals. The outcome of acquiring career skills is a significant increase in access to formal and dignified work opportunities for low skilled workers.

We have trained 70 students to date, achieving an average 4x earnings increase upon graduation. In one year, we aim to create employment for 1,000 workers by operating 16 cyber workstations across East Africa. In 5 years time, we expect to have created almost 500,000 jobs for low skilled workers across Africa. 

The majority of that number will have been achieved via our social franchising model by distributing data labeling training toolkits to computer labs across the continent to onboard low skilled workers onto self-regulated data labeling freelance platforms like Scale AI who automate pay, quality control, and new skills training. Although our estimates are conservative, we believe we can upskill millions of workers in distributed work by building an LMS tailored for low skilled workers in rural and urban areas. 

The rest will be achieved by soliciting direct contract work to build affordable distributed workforces for international companies, or charging data labeling companies to use our infrastructure to run cheap distributed workforces.

Our goals within the next year are to raise a seed round to train 1,000 workers across Africa (we have secured the work supply already) and do the following: 

• Invest in marketing and sales to solicit contracts from organisations interested in building in-house or distributed workforces

• Pilot blended learning to fully digitize our social franchising training toolkit and begin distributing it to partners

In the next five years, we will have achieved the following goals:

• Successfully built high quality, African data labeling workforces out of the most vulnerable youth for large, international clients in East, West, and South Africa (creating ~75,000 jobs), and looking to expand to other geographic regions outside of Africa

• Spread our social franchise toolkits & LMS platform to ~425,000 workers by marketing to schools and computer labs and equipping them to train community members 

The cyber workstation segment is how we achieve strong cash flows, and requires mastering operational complexity. However to reach millions and achieve scale, we must partner with other organisations via a social franchising model.

There are so many initiatives that promote technology in Africa that are being under-utilized because they are simply teaching  internet connectivity for knowledge acquisition instead of breaking the cycle of poverty. Given the abundance of data labeling platforms that automate quality control, pay, and job-specific trainings, it is possible for us to equip millions to access this work after they go through our training, which is is uniquely designed for people from the poorest backgrounds to understand.

 The barriers to us reaching our goals in the next year include if:

• We are unable to raise at least $250,000 seed round to accomplish our goals next year

• Covid-19 materially affects Kenya in a way that requires all businesses obeying social distancing rules to temporarily shut down for an extended period of time

• There is a material change in the supply of data labeling work available because large suppliers such as Tesla or Google declare bankruptcy

The barriers to reaching our goals over the next 5 years includes the following:

• Every African country we expand to has cultural differences that we must fully understand in order to build trust with communities, help the most vulnerable youth succeed, and tailor our pedagogy in a way that gets through to them

• Under-estimating or under-sizing the global demand for distributed data labeling workforce building would require us to pivot that segment of our business model to find utilisation of our cyber workstation infrastructure in another way, which would take extra funds and time

• A challenge of African expansion is that suppliers, employees, and beneficiaries in this market consistently try to take advantage of organisations perceived to be funded or connected. This is amplified in informal or low income areas in Africa where there are less formal procedures to get business done, and it can be a major obstacle in achieving optimal unit economics.

For barriers in the next year:

If we are unable to raise a large enough seed round, we will scale back the number of cyber workstations we plan to build, and continue as we have been to reach or goals on a shoestring budget at a slower rate.

We researched the business social distancing regulations and upgraded our workstations to be covid safe. We also engaged authorities to confirm we are abiding laws if covid escalates. Since we generate good salaries for 64 members of each community we operate in, we would be the last business asked to shut down in dire circumstances. 

If our partners' data labeling clients declare bankruptcy, we will seek out new data labeling work suppliers. We have an edge because if needed, we don’t have to charge a premium to companies since we generate revenue from the utilization of our workstation.

For barriers in the next 5 years:

In our expansion countries, we must always acquire talent that has a deep understanding of cultural differences, especially related to the challenges workers face and their community contexts. Without this, it would be impossible to tailor our pedagogy.

If we find that there is not a high demand for building cheap distributed workforces, we will have to explore an adjacent niche for our distributed infrastructure.

In response to price sabotage in low income areas, we mitigate this by hiring trusted & well referenced team members in new geographies use checks and balances to maintain an honest culture.

We are currently partnered with Scale AI, a silicon valley startup with a large, untapped supply of 3D lidar annotation work. Scale AI’s platform for distributed workers in emerging markets is called remotasks. We train the most underserved youth in lidar annotation for remotasks, and facilitate their continued access to data labeling work. We work with remotasks in the following areas:

·  Promote high quality taskers into reviewers, and subsequently super-reviewers

·  Train our staff to be experts in 3D lidar annotation

·  Data insights & monitoring into our workforce’s hours, tasks, accuracy, and pay

·  Piloting the effectiveness of varying pay schemes such as hourly, performance-based, etc.

Our business model provides value to 2 different types of customer:

1) We train low skilled workers in underserved communities to acquire a technology-related job, as well as the skills necessary to uplift them out of poverty

2) We build affordable, distributed workforces for large, international companies in need of data labeling work or data labeling companies in need of cheap distributed workers

We deliver trainings and facilitate access to data labeling work for low-skilled workers in one community at a time by renting a space and furnishing it with a minimum of 32 seats & laptops, as well as high speed wifi. After 12-14 months, each cyber workstation pays itself off and generates profit for the remaining 2.5 years of its life. The lifetime revenue per worker is $1,070 kes, while lifetime costs are only $620.

We begin with a 1.5 month training to train all 64 workers (if it is a 32 seat venue) in computer literacy, general employability, sex education for young women, and data labeling. Workers begin earning directly after the training in a day shift and nights shift. Workers are paid each week, and pay us a weekly fee to access our equipment. We partner with a data labeling employers to tailor the data labeling training to their needs.

Attracting the most ambitious but underprivileged workers in a community is one of our core competencies. This is why community engagement and geographic proximity of our workstations with communities is a core part of our model.

Our financial model projects breakeven in year 3, and revolves around operating cyber workstations in underserved communities that train and connect the community with data labeling companies in need of affordable, in-house workforces. Our revenue comes from three sources:

1) Each worker that utilizes the cyber workstation pays a $7 per week access fee (this amounts to <20% of their weekly salary)

2) Companies looking to build in-house data labeling workforces pay Jijenge to build them an affordable distributed workforce, or data labeling companies pay to access our infrastructure and run distributed workforces

3) Social franchising our digitized training course and data labeling toolkits to schools and computer labs across Africa. This generates revenue via franchise fees to access the toolkits to onboard workers.

 We plan to raise investment capital to cover expenses in year 1 and 2 before we can win larger contracts and achieve breakeven.

To date, Jijenge’s funding has been from donations in the amount of $63,000 USD over 2.5 years from various donors in the Boston tech entrepreneur community, as well as family and friends.

Jijenge started implementing its core revenue model at its first cyber workstation this month, in which $400 has been collected thus far. 

We aim to immediately raise at least $350,000 in grant capital to replicate the success of building workstations in other geographies, and validate an additional segment of our business model: Estimating the market size for building data labeling workforces for international companies, and acquiring contracts in this area.

Immediately after we accomplish that, Jijenge aims to raise an $800,000 seed round via grant and equity investors by the end of 2021.

Our estimated expenses for 2020 are $336,000 to produce $165,000 in revenue from setting up 16 new cyber workstations in East and South Africa, and to set the stage for social franchising and winning larger contract work.

Expenses consist of the following:

LMS Development for social franchising - $5,500

Cyber workstation set up & operating costs - $188,000

Employee salaries & contractors - $56,800

Sales, R&D expenses for workforce builder segment - $29,491

To provide clarity into the largest cost item (Cyber workstation set up & operating costs), the further breakdown of line items to setup 16 cyber workstations includes the following: 

- Asset purchase such as laptops and mouses

- Venue furnishing: Tables, chairs, outlets, surge protector, fire extinguisher, coffee burner

- Deposits for venues & monthly rent

- Utilities & wifi 24/7

- Day shift supervisor & trainer salary

- Night shift supervisor & trainer salary

- Monthly alarm fee for night shift

- Workstation supervisor

The most exciting thing about getting selected as a Solver is tapping into MIT's network of tech alumni. The data labeling space is a competitive space to break into, with lots of international players. It has been difficult to get informational interviews with data labeling companies to try to size the demand for data labeling workforce building. One of the last validation points of our business is if catering to that niche will produce the revenues required to become profitable, so getting help to figure that out as early as possible would save time, money, and resources. 

The prize funding is exactly what we need to catapult our expansion and replicate our success. After growing enough to produce $50,000 in revenue, we could open the gateway to raise equity funding and complete our pivot to a highly scalable, high impact growth startup. 

The marketing exposure and ethos from being as an MIT solver would also help us form connections, fundraise, and acquire large contracts to train workforces.

We would love to partner with companies or experts in the following areas:

• Distributed data labeling platform such as remotasks who are looking to expand in countries with cheap labor pools

• Grant writers or experts to maximize the amount of grant capital we could achieve to start pilots in more difficult African markets 

• Graphic designers, SEO optimisers or developers to help optimize our online presence to solicit data labeling contracts

• LMS platforms for us to partner with and piggyback off their platform instead of building our own to distribute our social franchising training toolkits

• Impact measurement consultants to quantify and expand analysis on the anecdotal impact evidence we have

• Experts well connected in the data labeling industry to offer wisdom and make us aware of the micro-niches and how much competition there is in each

We would like to provide services to organizations that need cheap but high quality data labeling workforces. Thus we would take advantage of MIT's network of alumni to source companies that would want to pilot this service with us. 

Additionally, we would want to partner with MIT faculty or initiatives to test new applications of machine learning used in experimental technologies. In this partnership, we could use our impact workforce to train a high volume dataset in a short period of time to assist the researcher in honing in on the accuracy and quality of the AI. 

We would also like to find a mentor among MIT faculty who are familiar with the international data labeling space, so we can build off their learnings in order to size the market and understand the micro-niches within that market. Since the sector is highly competitive, it is quite difficult to conduct market research without connections.