Data Scientist

What you will be expected to do 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 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 SQL skills - complex multi-table queries, window functions, performance optimization.Strong 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). 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). 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. Originally posted on Himalayas