Data Scientist

Job location: Remote

About the role:
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.

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scikit-learn, XGBoost, LightGBM, CatBoost. Proficiency with ML frameworks:

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PyMC or PyMC-Marketing. Solid understanding of probabilistic modeling, Bayesian inference, and uncertainty quantification; working experience with

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Python (pandas, NumPy, SciPy, matplotlib/seaborn/plotly, MLflow). High proficiency in

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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).

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.

Originally posted on Himalayas