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