Data Scientist

Department: Global Analytics and Technology

Employment Type: Permanent - Full Time

Location: India

Description

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.