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HEAD OF DATA SCIENCE

Posted 20.09.2020
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Company name: M-KOPA Solar
Industry: Other
Career level: Mid-level
Employment type: Full time
Job location: Mombasa, Mombasa
Salary offered Negotiable

Requirements

Education: Bachelor's Degree
Experience: 2-5 years
Languages: English
Driving license: No

Job role

Welcome to Jobweb Kenya. This website will help you achieve your career objective by linking you to vacancies from top Companies in Kenya and Africa. Job Seekers are also exposed to best articles for career growth, passing an interview and application writing with CVs. We strongly advise graduates not to pay money before getting a Job. Report fraudulent jobs to Company: Location: Kenya State: Job type: Full-Time Job category: MISSION M-KOPA’s mission is to make high quality energy affordable to everyone. OUR GROWTH SO FAR… M-KOPA has connected more than 400,000 homes in Kenya,Tanzania and Uganda to solar power with over 550 new homes being added every day. Each 8W battery powered-system comes with three lights, mobile phone-charging and a solar powered radio. Customers can now opt for a 20W system with digital TV. As of July 2016, M-KOPA has connected over 400,000 homes to affordable solar power. Current customers will make projected savings of US$ 300 Million over the next four years. Description The Head of Data Science will be a senior member in the M-KOPA Strategy and Data team and will drive M-KOPA’s data vision of being the smartest and most effective company in Africa. They will report directly to the Chief Strategy and Data Officer. The role will oversee all of M-KOPA’s data science and analytical engineering teams and depending on skills and experience, could also grow to oversee other data functions including business intelligence and data operations. LOCATION: Kenya, UK and/or remotely Responsibilities Lead and manage the data science and analytical engineering sections of the Strategy and Data team Help develop and steer the company’s overall data vision and strategy Drive data-driven decision making through the development of insights and efficiencies across the company Proactively identify the most important questions the business should be answering using data science and advanced analytics and then design and test data-driven hypotheses and then manage the team to test and execute against those In collaboration with our software development team, lead to the development of our data / analytical engineering pipelines; aggregating event-streams into question focused data sets Own the decisions and implementation of our data architecture and tool/stack selection Support the team in the building and operationalizing of machine learning models and in some circumstances, building some models yourself. Provide support and mentorship to all members of the team to develop their skills and capabilities Experience And Skills Experience: 4+ data experience, 2+ years of management experience Knowledge / Skills Required Experience managing data science/data engineering teams Advanced skills in Python or R, ideally both. Strong experience with SQL and SQL-inspired declarative query languages Ability to think creatively about business and engineering problems and understand how to apply data science processes to create measurable results Ability to communicate technical details visually and in written form to broader stakeholders Meticulousness in ensuring error-free, high-quality, reproducible analyses Experience with collaborative data and software development via git Additional assets Experience building predictive and explanatory models and putting them into production Experience with pythandas, airflow Experience with dbt (and Jinja) or a similar tool Experience with using automated deployment pipelines Experience with distributed computing tools such as Spark Experience with Microsoft Azure (Synapse Analytics, Data Factory, Data Lake, U-SQL), or other similar cloud providers and tools Familiarity with agile data ops development processes, unit testing, source control, continuous integration, etc. and their application to data workflows Experience with data visualisation tools (such as Power BI or Tableau, GGPlot2, D3.js, Seaborn, Matplotlib, Dash etc.) Experience with modern machine learning methods for signal processing / time-series analysis (e.g. HMMs, Kalman Filters, LSTM Neural Nets, etc.). Experience with advanced experiment design and causal methods for time series (e.g. casual inference based on BSTS, experiment design using multi armed bandits etc).

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