Head of Data Science at M-KOPA Solar

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).