|Date Posted||August 15, 2019|
Science & Engineering
Fenix International is a next-generation energy and technology company. Our mission is to improve the quality of life of our customers through inclusive energy and financial services. Our flagship product, Fenix Power, is a modular, lease-to-own solar home system financed through affordable installments from just $0.11 per day. We use real-time transaction data to create a next-generation credit score to finance power upgrades and other life-changing loans, unlocking modern financial services for our customers. To date, Fenix has sold over 500,000 Solar Home Systems in Uganda, Zambia, Cote d'Ivoire, Benin, Nigeria and Mozambique, bringing clean, reliable power for lights, phones, radios, TV and much more to over 2,500,000 people.
In early 2018, Fenix joined forces with ENGIE, one of the world's largest energy companies and a leader in the move to renewable, decentralised and digital energy. This has allowed Fenix to make significant commercial investments to accelerate the path to our mission, via new markets and innovative products. Together, Fenix and ENGIE are making universal access to modern energy a reality.
Deploy and maintain data pipelines and machine learning models to predict repayment, solar system device performance, credit scoring and other metrics which are key to the business.
Design randomized, controlled experiments to understand levers available to improve customer repayment.
Perform analyses, with statistical components as appropriate, for department leaders to evaluate the impact of pilots and other interventions.
Leverage external data sources to help Fenix grow its business and maximise social impact on customers in emerging markets.
Train team members on data tools / skills to grow data capacity and culture at Fenix.
Demonstrated expertise in strategic analysis to impact decisions.
Commitment to live and work in Uganda, East Africa
Thrives on teamwork.
Ability to communicate model outputs and analyses to stakeholders at various levels of technical expertise.
Bonus: Experience with financial data, credit scoring, IoT, and/or GIS data analysis
Strong Python, especially in a data analytics/science capacity (ex. pandas, numpy, sklearn, matplotlib)
Experience with experimental design, analysis, and interpretation of qualitative and quantitative data.
Experience deploying predictive models at scale.