Data Scientist

Job Description: Data Scientists

The candidate should be highly skilled Data Scientist with hands-on experience in building and deploying data-driven solutions. The ideal candidate should be comfortable working with large datasets, applying advanced statistical and machine learning techniques, and translating business problems into actionable insights.

Key Responsibilities

• Analyze large volumes of structured and unstructured data to extract meaningful insights.
• Build, validate, and deploy machine learning models for predictive analytics and automation.
• Design and implement machine learning pipelines for structured and unstructured data.
• Work closely with business stakeholders and cross-functional teams including data engineers, & analysts to translate problems into data-driven solutions.
• Build dashboards and data visualizations to communicate findings effectively.
• Conduct testing, hypothesis validation, and experiment tracking.
• Optimize model performance and ensure scalability and maintainability in production environments.
• Document methodology, workflow, and results clearly for future reference and compliance.
• Continuously monitor model performance and retrain based on data drift and feedback loops.

Required Skills & Tools

Category

Skills & Tools

Programming

Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.

Machine Learning

Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional).

Statistical Analysis

Hypothesis testing, regression models, Bayesian inference, multivariate analysis.

Data Engineering

Data preprocessing, cleaning, feature engineering, large dataset handling.

Big Data Tools

Spark, Hadoop, & Hive.

Model Deployment

MLflow, Docker, Kubernetes (for advanced roles).

Visualization

Power BI, Tableau, Plotly, Matplotlib, Seaborn.

Databases

SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).

Cloud Platforms

AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).

Version Control

Git, GitHub/GitLab.

Collaboration

Experience working with Agile teams, using tools like Jira, Confluence, Slack.

Good To have

• Experience with NLP, GenAI/LLMs, or recommendation systems is a plus.
• Familiarity with MLOps practices and tools (e.g., Kubeflow, Airflow) is a bonus.

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