DTICI_T8_Data_Scientist_Data_operations_R&D

The ideal candidate should possess strong expertise in statistical modeling, machine learning, AI, and emerging Agentic AI frameworks, along with experience in solving real-world automotive or manufacturing problems such as predictive maintenance, quality analytics, supply chain optimization, or connected vehicle use cases. Develop, validate, and deploy machine learning and AI models to solve business challenges. Apply statistical techniques (hypothesis testing, regression, Bayesian methods) to derive insights from complex datasets. Design and implement end-to-end data science pipelines , including data ingestion, feature engineering, model training, evaluation, and deployment. Build and operationalize Agentic AI systems (autonomous agents, multi-agent workflows, LLM-based reasoning systems). Work on time-series forecasting , anomaly detection, and predictive analytics for manufacturing/automotive use cases. Collaborate with cross-functional teams including data engineering, product, domain experts, and business stakeholders . Interface with IoT, telematics, MES, ERP, and connected vehicle platforms for data-driven insights. Ensure scalability and performance by deploying models using cloud-based solutions (Azure/AWS/GCP). Communicate findings effectively through visualizations, dashboards, and presentations . Stay current with advancements in AI/ML, including GenAI and Agentic AI ecosystems. Strong foundation in Statistics & Probability Hypothesis testing, regression models, A/B testing, Bayesian methods Expertise in Machine Learning Supervised & unsupervised learning, model tuning, ensemble techniques Hands-on experience with AI / Deep Learning NLP, computer vision, deep neural networks (preferred) Experience with Agentic AI / Generative AI LLMs (GPT, Llama, etc.), prompt engineering, RAG, autonomous agents Proficiency in Python (mandatory) Libraries: Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch Experience with Data Platforms Snowflake / Databricks / Spark / SQL Experience in Model Deployment APIs, Docker, MLflow, CI/CD pipelines Familiarity with Cloud Platforms Azure (preferred), AWS, or GCP Experience in Automotive or Manufacturing domain , including: Predictive maintenance Quality analytics & defect detection Supply chain optimization Production planning & optimization Connected vehicle / telematics analytics IoT data processing Strong analytical and problem-solving mindset Ability to explain complex models to non-technical stakeholders Excellent communication and storytelling skills Team-driven mindset with stakeholder management experience Preferred Qualifications :- Experience working with streaming data (Kafka, Spark Streaming) Knowledge of Digital Twins / Industry 4.0 concepts Exposure to MLOps frameworks Experience with graph-based AI or multi-agent systems Understanding of data governance and model explainability