Description : We are looking for a highly experienced Senior Data Scientist with strong hands-on expertise in AI/ML, data analytics, automation, and data extraction workflows. The ideal candidate will be responsible not only for building advanced AI models (Recommendation Systems, NLP, LLMs, Generative AI) but also for automating data ingestion, transformation, quality checks, and deriving actionable insights from complex datasets.

This role will work closely with engineering, product, and business teams to deliver scalable AI-driven and analytics-driven solutions for CIMETs platform.

Key Responsibilities :

AI/ML & Advanced Modelling :
- Design, develop, and optimize recommendation systems, personalization engines, and ranking algorithms.

- Build and deploy chatbots and NLP pipelines that automate customer interactions and support workflows.

- Develop and fine-tune Generative AI solutions, including LLM-based automation, content generation, and summarization tools.

- Research, customize, and operationalize Large Language Models (e.g., GPT, BERT, T5) for business-focused use cases.

- Perform advanced feature engineering, model interpretability, and bias mitigation.

Data Analytics & Business Insights :

- Perform deep data exploration, root-cause analysis, and pattern identification across large structured/unstructured datasets.

- Build dashboards, analytics models, and KPI-driven insights to support decision-making across business units.

- Translate raw data into actionable insights, highlighting trends, anomalies, forecasts, and opportunities.

- Work with business teams to understand analytical needs and develop automated reports, alerts, and insight pipelines.

Data Automation & Extraction :

- Design and build automated data extraction workflows using APIs, scraping frameworks, cloud pipelines, or ETL tools.

- Develop scalable ETL/ELT pipelines, automating ingestion, transformation, validation, and storage.

- Implement data quality checks, anomaly detection, and monitoring for automated pipelines.

- Collaborate with DevOps and Data Engineering teams to orchestrate workflows using Airflow, Prefect, AWS Glue, or similar tools.

Productionization & MLOps :

- Deploy ML models and analytics workflows to production with high availability and reliability.

- Work with engineering teams to integrate models and automation services into customer-facing products.

- Ensure CI/CD, automated testing, model retraining, and versioning through modern MLOps practices.

- Optimize model performance, scalability, and cost across cloud environments (AWS/GCP/Azure).

Collaboration, Leadership & Mentoring :

- Mentor junior data scientists and analysts, reviewing work, guiding techniques, and ensuring best practices.

- Collaborate with product managers to convert business problems into data-driven solutions.

- Work closely with engineering and DevOps teams to ensure alignment on data pipelines, APIs, and automation workflows.

- Drive innovation by evaluating new tools, frameworks, and AI/automation technologies.

Required Skills & Qualifications :

- 5+ years of hands-on experience in Data Science, Machine Learning, and AI-driven solution development.

Strong expertise in :

- Machine Learning (supervised, unsupervised, recommendation algorithms)

- NLP and Conversational AI (transformers, embedding models, chatbots)

- Generative AI (LLMs, prompt engineering, fine-tuning)

- Solid background in data analytics, statistical analysis, and insight generation.

- Hands-on experience in automating data extraction (APIs, scraping frameworks, cloud ingestion services).

- Experience developing ETL/ELT pipelines using SQL, Python, and cloud-based orchestration tools.

- Proficiency in Python and associated libraries : Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, HuggingFace, NLTK.

- Strong SQL skills and experience with relational and NoSQL systems (MySQL, PostgreSQL, MongoDB).

- Experience deploying models on cloud platforms (AWS, Azure, GCP).

- Strong understanding of MLOpsCI/CD, Docker, Kubernetes, model monitoring, and retraining pipelines.

Preferred Qualifications :

- Experience with Reinforcement Learning or advanced personalization techniques.

- Exposure to automating business workflows, rule engines, or intelligent decision systems.

- Experience with A/B testing, experimentation platforms, and performance measurement for ML models.

- Knowledge of ethical AI, privacy, and security considerations for data and models.

- Strong communication and presentation skills; ability to simplify complex outputs for non-technical stakeholders.

- Prior experience leading or mentoring AI/ML engineers and analysts.