Data 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.

We are looking for 24 Fresher Data Analyst / Data Science / Python / IT INDUSTRIES Posts in Mumbai, Maharashtra,Bangalore, Karnataka,Kolkata, West Bengal,Jaipur, Rajasthan,Hyderabad, Telangana,Ahmedabad, Gujarat,Pune, Maharashtra,Chandigarh,Patna, Bihar,Ranchi, Jharkhand,Data Analyst,Data Scientist,Python Developer,Microsoft Excel,SQL Data Base Administrator, with deep knowledge in Data Analyst,Data Scientist,Python Developer,Microsoft Excel,SQL Data Base Administrator and Required Educational Qualification is : Higher Secondary, Secondary School, Diploma, Professional Degree, Other Bachelor Degree, B.A, B.C.A, B.Com Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary..