Job Vacancies 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).

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The analytics engineer will multiply the effectiveness of everyone in the Data and BI team by creating, sharing, and making easy the data systems that workflows are built on, whilst contributing positively to team culture. Our vision is to be the smartest company in Africa and you will play a crucial part in that.

Analytic engineering at M-KOPA is new and evolving, sitting at the intersection of Data Science, BI/Analytics, and Data Engineering. You’ll sit inside a small team with autonomy to improve the data teams workflows. Analytics engineering is a relatively new field, so we welcome applicants from Data Analysts who tend towards repeatability and automation to Data Engineers who are interested in the business domain, data scientists who’ve realized that clean data is more important than another 1000 epochs, or software engineers who want the perfect entry into the data space.

Location: Kenya, UK, Or Remote Working

Responsibilities

• Ultimately the responsibility is to increase the speed and efficiency of common data team workflows. Below are some examples of how you may achieve this
• Improve the overall Data team’s workflow through knowledge sharing, proper documentation, and code review
• Deliver/review new automation frameworks within the team
• Work on efficient ingestion of new data into our data warehouse using tools such as Python, Spark, Airflow, ADF, Databricks, Azure Data Lake, etc
• Work on efficient storage of our data in the data warehouse, identifying performance improvements from query to table redesign.
• Work on the careful design of the schema’s, table names, data models, and practices within the data warehouse, creating a well-curated data set.
• Rewrite our data model using dbt or similar, and empower other analysts to use the frameworks, developing their skills with mentoring and good code review.
• Identify re-usable elements of downstream analytics and move into the repeatable data model
• Contribute to our internal python and R libraries, driving best practice

EXPERIENCE AND SKILLS

Experience: 3+ years of data experience;

Knowledge / Skills Required

• You are a structured thinker, able to implement structure to simplify workflows and enable teams
• You are curious, from new tools in the data world to what is the right business definition for this metric
• Strong SQL Skills and transforming data
• Experience with collaborative data and software development via git and you’re comfortable in the command line
• Experience deploying code/models to production, ideally via automated deployments
• Experience with Data Warehouse Schema Design

Additional assets

• Experience with python, pandas, 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 (U-SQL, Data Factory, Data Lake), other Big Data tools (Hadoop, Spark), similar cloud providers and tools also a plus
• Familiarity with agile data ops development processes, unit testing, source control, continuous integration, etc.
• Experience with data visualization tools (such as Power BI or Tableau, GGPlot2, D3.js, Seaborn, Matplotlib, Dash, etc.)

COMPENSATION:

Competitive package covering a monthly salary, performance bonus and medical benefits reflective of the candidate’s experience and skills.