Senior Data Scientist: Behavior & Analytics

About Us:

UpEnergy delivers high-impact, emission reducing projects that accelerate decarbonization and create long term benefits in local communities. We do so by making life-improving clean technologies available to local communities and environments across Africa. Whether it’s protecting the local environment, delivering health benefits, or reducing poverty through financial savings and employment, our projects sustainably benefit and enable people to live better lives within climate resilient communities.

To date, UpEnergy has delivered over 95 emission reduction projects, impacted over 4 million lives, and reduced over 4 million tons of CO2 Emissions across 7 countries. UpEnergy prides itself on deep local knowledge and established teams on the ground to ensure the highest impact and results. The UpEnergy team continues to grow, across multiple countries and continents, and seeks diligent, entrepreneurial, fun, and motivated talent who are excited to join us in delivering even more impact.

Learn more here: www.upenergygroup.com 

About the role:

UpEnergy seeks a Senior Data Scientist to own and operationalize UpEnergy’s behavioral intelligence layer - translating IoT, pilot, and third-party data into decision systems that shape product design, behavioral interventions, and financial performance across product lines.

This role is accountable not just for analysis, but for ensuring behavioral insights are used - embedded into product, operations, carbon, and commercial decisions

Core Mandates

This role has two primary mandates, enabled by a third technical capability:

1. Organizational Data Visibility & Decision Infrastructure
    Govern how behavioral and product data is defined, surfaced, and used across the organization.
2. Behavioral Intelligence & Experimentation
   Generate and validate behavioral insight that informs intervention, product, and scaling decisions.
3. Advanced Analytics & Modelling (Enabling Capability)
   Apply statistical and predictive methods in service of the above mandates.

Key Decisions this Role informs (not exhaustive) 

• Which behavioral interventions are scaled, adapted, or stopped
• How product features and defaults are prioritized based on usage dynamics
• How usage, adoption, and relapse risk affect:
  • Carbon revenue confidence
  • Subsidy design
  • Distribution and after-sales strategies
• How resources are allocated across pilots, geographies, and product lines
• Which behavioral and product data signals are strategically relevant to position UpEnergy as an industry leader in the clean cooking transition

A. Behavioral Intelligence & Experimentation

• Co-own the behavioral intelligence function alongside Behavioral Science, ensuring hypotheses, interventions, and measurements are tightly coupled to business and unit-economic outcomes
• Translate behavioral hypotheses into measurable leading indicators (behavior → adoption → sustained use → economic impact)
• Define minimum viable evidence required for go / no-go decisions on pilots and interventions
• Balance statistical rigor with operational timelines, prioritizing decision relevance over academic completeness

1. Data Preparation & Modelling for Behavioral Studies

• Develop analytic‑ready datasets from the existing data warehouse for pilots and studies.
• Build derived behavioral variables, including:
  • Usage frequency, consistency, and streaks
  • Time to first use / adoption curves
  • Non‑usage streaks and relapse behavior
  • Behavioral and customer cluster segmentation (e.g., early adopters, sustained users, drop‑offs)
• Implement automated data cleaning and transformation scripts to reduce reliance on manual extraction.

2. Advanced Analytics & Modeling (Enabling Capability)

• Select appropriate analytical methods based on decision risk, data quality, and timelines..
• Conduct descriptive and inferential analysis, including:
  • Linear and logistic regression for drivers of adoption and sustained use
  • Time‑series analysis for seasonality and usage dynamics
• Build and deploy predictive models (e.g., churn, usage probability, repayment risk) using Python within the AWS environment.

3. Experimental Data Infrastructure

• Design data marts or schemas to track:
  • Pilot metadata (control vs treatment, intervention types)
  • Outcomes (usage, repayment, retention)
• Partner with Data Engineering to automate ETL pipelines for pilot and behavioral datasets.

B. Organizational Data Visibility & Decision Infrastructure

1. Analytics Strategy & Ownership

• Own the analytics layer sitting between data engineering and business users.
• Define how behavioral and product data is surfaced, structured, and consumed across Product, Operations, Carbon, Commercial, and Leadership
• Ensure analytics outputs are consistent, trusted, and aligned with the single source of truth.

2. Metric Governance & Shared Language

• Define and maintain canonical behavioral and product metrics and their economic interpretation
• Enforce metric consistency across dashboards, reports, and teams
• Establish standards for what is tracked, what is deprecated, and what is intentionally not tracked

3. Data Visibility, Dashboards & Insights

• Design decision-oriented analytics products (e.g., Tableau dashboards, curated datasets, metric frameworks) covering:
  • Product usage and engagement
  • Pilot and cohort performance
  • Behavioral segments over time
  • Core operational and strategic KPIs
• Ensure dashboards move teams from descriptive reporting to diagnostic and prescriptive insight

4. Decision‑Making Enablement

• Support Product and Leadership with deep‑dive analyses that inform:
  • Product development and iteration
  • Pilot scaling or termination decisions
  • Resource allocation and strategy
• Proactively identify patterns, risks, and opportunities in the data.

Required Experience & Skills

Preferred Background

• Experience in FMCG, consumer products, or large-scale distribution businesses where behavior, usage, and retention drive economics
• Experience working with imperfect behavioral data (e.g., IoT, telemetry, surveys, proxies)
• Exposure to emerging markets or impact-driven programs where experimental purity must be balanced with operational reality

Experience

• 5–10+ years in data science, applied analytics, or quantitative research roles.
• Proven experience owning analytics end‑to‑end, not just modeling.
• Experience working with experimentation, pilots, or behavioral / customer analytics.
• Has data science projects under his/her belt.
• Proven experience with cloud platforms like AWS is a plus

Technical Skills

• Advanced Python (pandas, numpy, statsmodels, scikit‑learn).
• Strong statistical foundations (experimental design, inference, regression, time‑series).
• Experience with cloud environments (AWS preferred).
• Ability to model data using SQL.
• Data storytelling skills using BI tools such as Tableau.
• Experience building production‑ready analytics pipelines.
• Ability to manage code versions using Git.
• Data wrangling using Excel is a plus.

Analytics & Business Skills

• Ready to roll up your sleeves
• Ability to translate complex analysis into clear, actionable insights.
• Strong judgment on what analysis is “good enough” vs over‑engineering.
• Comfort operating across strategy, product, and technical domains.

Why Join Us? 

• Not your average 9-to-5
  • UpEnergy encourages teamwork, creativity, and we’re not afraid to get our hands dirty. We stay close to our market, understand our customers, and have a lot of fun while doing our work! If you’re looking for a conventional 9-to-5, you’d be missing out on our adventures!
• Opportunity to do things in a new way
• • As one of the largest compliance credit issuer in Africa, we need to be constantly improving and rethinking our way of work. This means endless opportunities for a self-starter to initiate new ways of doing things or experimenting ways to help us improve.
• Personal and Career Growth
• • Wouldn’t it be a pity to spend so much time working but not growing and learning? At UpEnergy we value both personal and professional growth, and we want to grow as a team. From classroom training to coffee chats to performance review, we want to support every team member to grow into the best version they can.
    
• Create Lasting Impact
• • Our clean energy products bring positive impact to both our planet earth and for the low-income communities we work in. We don’t just focus on sales – we follow up and truly understand our customers to make sure that they continue to benefit from our products. Your work will create much longer-lasting impact than what you can see!