Scientist Job Vacancies in Mohali

Role Overview Were hiring a Senior Data Scientist who can own end-to-end problem solving-from business discovery and hypothesis design to model deployment and post-production monitoring. You will partner with product, engineering, and client stakeholders to build production-grade ML/AI and GenAI solutions on AWS/Azure/GCP and mentor a small pod (2-5) of data scientists/ML engineers. Key Responsibilities Business & Problem Framing: Engage with client stakeholders to translate objectives into measurable DS/ML use cases, define success metrics (ROI, adoption, accuracy, latency), and create experiment plans. Data Strategy & Feature Engineering: Own data acquisition, quality checks, EDA, and feature pipelines across SQL/Spark/Databricks; collaborate with Data Engineering for robust ingestion and transformation (Airflow/dbt). Modeling: Build, tune, and compare models for supervised/unsupervised learning, time-series forecasting, NLP/CV, and GenAI (RAG, fine-tuning, prompt-engineering) using Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM), PyTorch/TensorFlow, Hugging Face. MLOps & Deployment: Productionize via MLflow/DVC, model registry, CI/CD (GitHub/GitLab), containers (Docker/Kubernetes), and cloud ML platforms (SageMaker/Azure ML/Vertex AI). Expose services via FastAPI/Flask; implement monitoring for drift, data quality, and model performance. Experimentation & Causality: Design and analyze A/B tests, apply causal inference techniques (e.g., propensity scoring, DiD) to measure true impact. Explain ability, Fairness & Compliance: Apply model cards, SHAP/LIME, bias checks, PII handling, anonymization/pseudonymization, and align with applicable data privacy regulations (e.g., GDPR/DPDP). Visualization & Storytelling: Build insights dashboards (Tableau/Power BI/Plotly) and communicate recommendations to senior business and technical stakeholders. Collaboration & Leadership: Mentor juniors, conduct code and research reviews, contribute to standards, and support solutioning during pre-sales/POCs. Required Skills & Experience Experience: 7-10 years overall, with 5+ years in applied ML/Data Science delivering models to production for enterprise clients. Programming & Data: Expert Python, advanced SQL, and hands-on with Spark/Databricks. Strong software practices (testing, typing, packaging). ML/AI Stack: scikit-learn, XGBoost/LightGBM; PyTorch or TensorFlow; NLP (spaCy, Transformers, embeddings), vector DBs (FAISS/Pinecone), LangChain/LlamaIndex for RAG. Cloud & MLOps: Real-world deployments on AWS/Azure/GCP using SageMaker/Azure ML/Vertex AI; MLflow, model registry, feature store, Docker/K8s, and CI/CD. Experimentation & Analytics: A/B testing, Bayesian/ frequentist methods, causal inference, statistical rigor. Visualization & Communication: Storytelling with data; Tableau/Power BI/Plotly, executive-ready presentations. Domain Exposure (nice-to-have): BFSI risk/collections/CLV, retail demand/personalization, healthcare claims/clinical NLP, manufacturing quality/predictive maintenance. Bonus: Recommenders, time-series, graph ML, optimization (OR), reinforcement learning, geospatial analytics. Education & Certifications Bachelors/Masters in Computer Science, Data Science, Statistics, Applied Math, or related field. Preferred certifications: AWS/Azure/GCP ML, Databricks, TensorFlow or PyTorch.

Key Responsibilities:Developing ML/AI Models: Design, implement, and deploy machine learning algorithms and models using Python libraries such as TensorFlow, PyTorch, or scikit-learn.Data Preprocessing and Analysis: Clean, preprocess, and analyze large datasets to derive actionable insights for model development.Building APIs and Integrations: Develop RESTful APIs and integrations for deploying ML models into production environments.Performance Optimization: Optimize algorithms and models for scalability, speed, and accuracy.Collaboration: Work closely with data engineers, software developers, and business stakeholders to understand requirements and deliver ML/AI solutions.Model Testing and Validation: Conduct rigorous testing and validation of ML models to ensure robustness and reliability.Documentation: Document code, processes, and experiments to facilitate knowledge sharing and collaboration.Continuous Learning: Stay updated with the latest advancements in ML/AI technologies and frameworks.Required Skills and Qualifications:Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or related field.Proven experience (minimum 3 years) as a Python Developer with expertise in building and deploying ML/AI solutions.Strong programming skills in Python and proficiency with relevant libraries (e.g., TensorFlow, PyTorch, pandas, NumPy).Solid understanding of machine learning algorithms (e.g., supervised/unsupervised learning, deep learning, reinforcement learning).Experience with data preprocessing, feature engineering, and model evaluation.Proficiency in deploying ML models in cloud environments (e.g., AWS, Azure, Google Cloud).Excellent problem-solving skills and ability to think critically.Strong communication skills and ability to collaborate effectively within a team.Experience with version control systems (e.g., Git) and agile development practices.