Senior Python Developer (Data Science / Machine Learning)

Senior Python developer (8–10 years) needed to design and build scalable Python services and APIs while also owning data science/ML work end-to-end: data cleaning/EDA/feature engineering, model training and evaluation, and integration/deployment of ML models into Python applications. Strong SQL and relational database skills are required, along with experience building data pipelines and optimizing performance on large datasets. Preferred exposure includes GenAI/LLM tooling (RAG, embeddings, vector DBs, LangChain/LlamaIndex/Hugging Face) and cloud/DevOps (AWS/Azure/GCP, Docker, CI/CD, Kubernetes).

Responsibilities

• Design, develop, and maintain scalable applications and services using Python.
• Develop reusable, maintainable, and high-quality Python code following industry best practices.
• Work with structured and unstructured data for data analysis, processing, and modeling.
• Perform data cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and statistical analysis.
• Develop, train, evaluate, and optimize Machine Learning models based on business requirements.
• Implement and integrate ML/Data Science models into Python-based applications.
• Work with libraries and frameworks such as Pandas, NumPy, Scikit-learn, SciPy, Matplotlib/Seaborn, and other relevant data science tools.
• Develop data pipelines and automate data processing workflows.
• Work with APIs, databases, and external data sources to collect and process data.
• Optimize application and data-processing performance for large datasets.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, Product Owners, and business stakeholders.
• Translate business problems into technical and data-driven solutions.

Requirements

• Python (8–10 years)
• Strong Python fundamentals: OOP, data structures, exception handling
• Design patterns and coding best practices/SDLC
• REST API development (FastAPI or Flask or Django)
• Unit testing, debugging, logging
• Performance optimization (applications and data processing)
• Git/version control
• Data cleaning and preprocessing
• Feature engineering
• Exploratory Data Analysis (EDA)
• Statistical analysis/statistical concepts
• Machine Learning: regression, classification, clustering
• Model evaluation/validation and hyperparameter tuning
• Pandas
• NumPy
• Scikit-learn
• SciPy
• Matplotlib and/or Seaborn
• SQL (strong)
• Relational databases (PostgreSQL or MySQL or SQL Server or Oracle)