Data Scientist – AI/ML 3-5 years work experience

Role Overview

Data Scientist / Machine Learning Engineer

You will work closely with product, engineering, and business stakeholders to build, deploy, and maintain data-driven solutions across forecasting, recommendation, classification, and anomaly detection use cases.

Key Responsibilities

Data Science & Modeling

• Understand business problems and convert them into

  ML problem statements

• Perform

  EDA, feature engineering, and feature selection

• Build and evaluate models using:
• Regression, classification, clustering
• Time-series forecasting
• Anomaly detection and recommendation systems
• Apply model evaluation techniques (cross-validation, bias-variance tradeoff, metrics selection)

ML Engineering & Deployment

• Productionize ML models using

  Python-based pipelines

• Build reusable training and inference pipelines
• Implement model versioning, experiment tracking, and retraining workflows
• Deploy models using APIs or batch pipelines
• Monitor model performance, data drift, and prediction stability

Data Engineering Collaboration

• Work with structured and semi-structured data from multiple sources
• Collaborate with data engineers to:
• Define data schemas
• Build feature pipelines
• Ensure data quality and reliability

Stakeholder Communication

• Present insights, model results, and trade-offs to non-technical stakeholders
• Document assumptions, methodologies, and limitations clearly
• Support business decision-making with interpretable outputs

Required Skills

Core Technical Skills

• Programming:

  Python (NumPy, Pandas, Scikit-learn)

• ML Libraries:

  XGBoost, LightGBM, TensorFlow / PyTorch (working knowledge)

• SQL:

  Strong querying and data manipulation skills

• Statistics:

  Probability, hypothesis testing, distributions

• Modeling:

  Supervised & unsupervised ML, time-series basics

ML Engineering Skills

• Experience with

  model deployment

  (REST APIs, batch jobs)
• Familiarity with

  Docker

  and CI/CD for ML workflows
• Experience with

  ML lifecycle management

  (experiments, versioning, monitoring)
• Understanding of

  data leakage, drift, and retraining strategies

Cloud & Tools (Any One Stack is Fine)

• AWS / GCP / Azure (S3, BigQuery, SageMaker, Vertex AI, etc.)
• Workflow tools: Airflow, Prefect, or similar
• Experiment tracking: MLflow, Weights & Biases (preferred)

Good to Have

• Experience in domains like

  manufacturing, supply chain, fintech, retail, or consumer tech

• Exposure to

  recommendation systems, forecasting, or optimization

• Knowledge of

  feature stores

  and real-time inference systems
• Experience working with large-scale or noisy real-world datasets