Data Scientist

Role Overview We are looking for a versatile Data Scientist who can build robust data pipelines — from web scraping to AI/ML deployment. The ideal candidate is comfortable working across the full data and AI lifecycle: extracting data at scale, transforming and storing it reliably, and using it to train, deploy, and maintain machine learning models in production. Key Responsibilities Design, build, and maintain scalable web scraping scripts/pipelines using Python (e.g., BeautifulSoup, Scrapy, Selenium, Playwright) Handle dynamic websites, pagination, anti-bot mechanisms, proxies, and rate-limiting strategies Clean, transform, and normalize scraped data (structured/unstructured) before storage Design MongoDB schemas and collections optimized for the type of data being handled Implement logic to identify and update unique/duplicate records efficiently (upserts, deduplication strategies) Schedule and monitor data pipelines/jobs (via cron, Airflow, or similar orchestration tools) Ensure data quality, consistency, and integrity across pipelines, including error logging, retries, and failure recovery Support end-to-end AI/ML lifecycle: data collection, preprocessing, feature engineering, model selection, training, and validation Fine-tune machine learning/deep learning models and evaluate performance against business requirements Package and deploy models into production environments (APIs, batch pipelines, etc.) Implement and maintain MLOps practices — versioning (models & data), CI/CD for ML, monitoring model performance/drift Collaborate with cross-functional teams to integrate AI models with existing data pipelines, including scraped/transformed data as model input Required Skills Core: Strong proficiency in Python (writing clean, modular, production-grade code) Hands-on experience with MongoDB (schema design, aggregation pipelines, indexing, upsert/dedup logic) Web scraping tools/libraries: BeautifulSoup, Scrapy, Selenium, Playwright , or similar Data transformation/manipulation using Pandas / NumPy AI/ML: Understanding of the end-to-end AI/ML workflow — data prep, training, evaluation, deployment Familiarity with ML/DL frameworks: Scikit-learn, TensorFlow, PyTorch Exposure to MLOps tools : MLflow, DVC, Docker, Kubernetes (basic), CI/CD pipelines Experience with model deployment (REST APIs via FastAPI/Flask , or cloud ML services) Good to Have: Experience with cloud platforms ( AWS / GCP / Azure ) for storage, compute, and ML services Familiarity with LLMs / NLP (e.g., Hugging Face, LangChain) if relevant to use case Knowledge of proxy rotation, CAPTCHA-handling, and anti-scraping evasion techniques Experience with workflow orchestration ( Airflow, Prefect ) Version control ( Git ) and Agile development practices Soft Skills Strong problem-solving mindset, especially around handling unstructured/messy data Ability to independently manage multiple workstreams across data engineering and AI/ML Good documentation habits for pipelines and models Comfortable working in a fast-paced, evolving tech environment Qualifications Bachelor's/Master's degree in Computer Science, Data Science, Engineering, or related field Prior experience with production-grade web scraping and/or ML systems is a strong plus Portfolio/GitHub showcasing scraping projects and/or ML models is preferred