Principle Scientist (Data Analytics)

Job Summary

We are seeking a skilled and motivated Data Scientist with 2–3 years of hands-on experience in machine learning and applied statistics to join our growing analytics team. The ideal candidate will have a strong foundation in statistical analysis, ML algorithms, model development, and deployment, with a passion for solving real‑world problems using data‑driven and statistically sound approaches

Responsibilities

1. Machine Learning & Statistical Modeling

· Design, build, and evaluate supervised and unsupervised ML models (e.g., regression, classification, clustering, recommendation systems).

· Apply statistical modeling techniques such as linear/logistic regression, regularization, Bayesian methods, and time‑series analysis where appropriate.

· Perform feature engineering, model tuning, and validation using cross‑validation, statistical tests, and performance metrics.

2. Data Preparation, EDA & Statistical Analysis

· Clean, preprocess, and transform large datasets from multiple structured and unstructured sources.

· Conduct exploratory data analysis (EDA) using descriptive statistics, distributions, correlation analysis, and data visualization.

· Use inferential statistics (hypothesis testing, confidence intervals, A/B testing) to support modeling decisions and business insights.

3. Model Evaluation, Deployment & Monitoring

· Evaluate models using both ML metrics (accuracy, precision‑recall, AUC, RMSE) and statistical measures.

· Deployment of ML models into production using tools such as Flask, FastAPI, or cloud‑native services will be an add on.

· Monitor model performance, data drift, and statistical stability; retrain or recalibrate models as required.

4. Collaboration & Communication

· Work closely with data engineers, product managers, and business stakeholders to translate business problems into statistically and analytically sound solutions.

· Communicate results, assumptions, and limitations of models clearly to both technical and non‑technical audiences.

5. Tools & Technologies

· Use Python and libraries such as NumPy, pandas, SciPy, scikit‑learn, XGBoost, TensorFlow, or PyTorch.

· Utilize visualization and analytics tools (Matplotlib, Seaborn, Plotly) for statistical reporting.

· Leverage version control (Git), Jupyter notebooks, and ML lifecycle tools (MLflow, DVC).

Preferred Qualifications

· Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.

· 3–4 years of experience in building, evaluating, and deploying ML models.

· Strong programming skills in Python; working knowledge of SQL.

· Solid foundation in statistics, including probability theory, hypothesis testing, regression analysis, and experimental design.

· Exposure to cloud platforms (AWS, GCP, or Azure) and MLOps practices is advantageous.

· Excellent analytical thinking, problem‑solving, and communication skills.