The Data Scientist is responsible for designing, developing, and deploying advanced analytics, machine learning, and statistical models to solve complex business problems. This role partners closely with data engineering, product, and business teams to transform data into actionable insights that drive strategic decisions and measurable business outcomes.

Key Responsibilities

Data Exploration & Analysis

Explore, clean, and analyze large structured and unstructured datasets using statistical methods.
Identify patterns, trends, and key drivers to inform product, operational, and strategic decisions.
Conduct hypothesis testing, A/B experiments, and predictive analytics.

Machine Learning & Modeling

Build and deploy machine learning models (classification, regression, clustering, NLP, forecasting).
Select appropriate algorithms and optimize model performance using feature engineering, hyperparameter tuning, and model evaluation techniques.
Develop scalable ML pipelines in collaboration with data engineering.

Production Deployment & Optimization

Work with ML engineers or data engineers to deploy models into production environments.
Monitor model performance, implement retraining strategies, and ensure model lifecycle management.
Apply MLOps best practices including version control, reproducibility, and automation.

Collaboration & Stakeholder Engagement

Translate business requirements into data-driven solutions and clearly communicate insights to stakeholders.
Collaborate with product managers, analysts, and domain experts to define success metrics and project goals.
Present findings to executives and non-technical audiences in a compelling, digestible way.

Tools, Technologies & Technical Expertise

Programming: Python, R, SQL
ML & Data Science Frameworks: scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM
Data Tools: Spark, Databricks, Snowflake, BigQuery, Redshift, Pandas
Visualization: Tableau, Power BI, Plotly, Matplotlib, Seaborn
Cloud Platforms: AWS, Azure, GCP
Experience with NLP, Generative AI, LLM fine-tuning, or deep learning (optional but valuable)

Qualifications

Required

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field.
10+ years of hands-on experience in data science or applied machine learning.
Strong foundation in statistics, probability, and mathematical modeling.
Proficiency in Python or R and advanced SQL.
Experience deploying ML models and working with cloud-based ecosystems.

Preferred