We are looking for a skilled Data Scientist who can translate complex datasets into actionable business insights through rigorous statistical analysis and machine learning. The ideal candidate combines strong foundational knowledge of classical ML with a solid grasp of probabilistic and Bayesian modeling, and can operate effectively across the full spectrum from data exploration to production-ready model delivery.
What you will be expected to do
KEY RESPONSIBILITIES
Design, build, and evaluate classical machine learning models for business-critical use cases (classification, regression, ranking, anomaly detection, time-series forecasting).
Apply probabilistic and Bayesian modeling techniques to quantify uncertainty and inform decision-making under uncertainty; leverage tools like PyMC and PyMC-Marketing for Bayesian workflows.
Perform rigorous EDA, feature engineering, and data wrangling on large structured and semi-structured datasets using Python and SQL.
Collaborate with data engineers and analytics engineers to source, clean, and validate data pipelines feeding ML workflows.
Develop, track, and communicate model performance metrics; identify degradation signals and recommend retraining or improvement strategies.
Translate business questions into well-framed statistical problems and present findings clearly to technical and non-technical stakeholders.
Maintain clean, reproducible, and well-documented code and notebooks following team engineering standards.
You might be a strong candidate if you have/are
REQUIRED SKILLS & QUALIFICATIONS
3–4 years of hands-on experience in a data science or applied ML role.
Strong command of classical ML algorithms - gradient boosting, random forests, SVMs, logistic regression, clustering, dimensionality reduction, etc.
scikit-learn, XGBoost, LightGBM, CatBoost.Proficiency with ML frameworks:
PyMC or PyMC-Marketing.Solid understanding of probabilistic modeling, Bayesian inference, and uncertainty quantification; working experience with
Python (pandas, NumPy, SciPy, matplotlib/seaborn/plotly, MLflow).High proficiency in
Deep familiarity with model evaluation frameworks: cross-validation, calibration, AUC, RMSE, MAPE, lift/gain curves, and business-aligned metrics.
Experience with experiment design, A/B testing, and statistical hypothesis testing.
Comfortable working with cloud data warehouses (AWS Redshift, BigQuery, Snowflake) and standard ML experiment tracking tools (MLflow, W&B).
NICE TO HAVE
Exposure to survival modeling, causal inference, or marketing mix modeling (MMM).
Experience with time-series forecasting libraries (Prophet, statsmodels, sktime).
Prior work in fintech, PAYG, or emerging markets contexts.
Familiarity with MLOps pipelines and model deployment on AWS (SageMaker, Lambda, ECS).
EDUCATION
B.Tech / B.E. / B.Sc. / M.Tech / M.Sc. in Computer Science, Statistics, Mathematics, Engineering, or a closely related quantitative discipline.
What Sun King offers
Professional growth in a dynamic, rapidly expanding, high-social-impact industry
An open-minded, collaborative culture made up of enthusiastic colleagues who are driven by the challenge of innovation towards profound impact on people and the planet.
A truly multicultural experience: you will have the chance to work with and learn from people from different geographies, nationalities, and backgrounds.
Structured, tailored learning and development programs that help you become a better leader, manager, and professional through the Sun King Center for Leadership.
About Greenlight Planet
Powering access to brighter lives in Africa, Asia, and beyond
