Data Scientist

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.
• Proficiency with ML frameworks: scikit-learn, XGBoost, LightGBM, CatBoost.
• Solid understanding of probabilistic modeling, Bayesian inference, and uncertainty quantification; working experience with PyMC or PyMC-Marketing.
• High proficiency in Python (pandas, NumPy, SciPy, matplotlib/seaborn/plotly, MLflow).
• Strong SQL skills - complex multi-table queries, window functions, performance optimization.
• 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.