The Data Scientist, D&T will be part of a multidisciplinary team responsible for designing and developing innovative data-driven analytical solutions.

This role will leverage advanced analytics, Machine Learning, NLP, Computer Vision, and Generative AI to address complex business challenges and deliver measurable value.

Essential Duties and Responsibilities :

- Partner with stakeholders across functions to uncover and prioritize use cases where ML, statistical modeling, or GenAI/Agentic AI solutions can create business impact.

- Lead end-to-end model/agent lifecycles : from problem definition, data exploration, feature engineering, and model/agent design to deployment, monitoring, and continuous improvement.

- Ingest, integrate, and preprocess large-scale structured, semi-structured, and unstructured datasets, ensuring high standards of data quality and integrity.

- Develop and enhance scalable data pipelines and infrastructure; proactively detect and mitigate data drift, bias, or quality gaps.

- Drive development of specialized computer vision solutions (e., object detection, classification, OCR, segmentation) for business-critical applications such as manufacturing, quality control, and compliance monitoring

- Design and operationalize CI/CD and AgentOps workflows : model/dataset versioning, reproducibility, automated testing, deployment, rollback, and governance.

- Define, implement, and monitor evaluation metrics (accuracy, robustness, fairness, latency, safety, etc.) for both offline and production environments.

- Conduct error analysis, monitor live model/agent performance, and ensure compliance with AI governance and ethical standards.

- Create intuitive dashboards, reports, and data visualizations (e., Power BI, Fabric) to communicate insights to technical and non-technical stakeholders.

- Translate technical findings into actionable business recommendations; clearly communicate risks, performance, and opportunities to decision-makers.

- Collaborate closely with BI, engineering, and domain experts to operationalize data-driven solutions at scale.

- Awareness of compliance, SOPs, and ethical AI practices in enterprise environments.

- Working knowledge of CI/CD, MLOps and AgentOps practices : reproducible pipelines, monitoring, governance with telemetry and observability.

Additional Responsibilities :

Education : Bachelor's or master's degree in computer science, Mathematics, Statistics, Data Science, or related quantitative field.

Work Experience : 3 - 6 years of relevant experience in data science, machine learning, or applied AI roles.

Preferred Knowledge, Skills and Abilities :

- Strong proficiency in Python, SQL and prompt engineering for data manipulation, analysis, and querying large datasets.

- Context engineering and proficiency in executing GenAI projects at scale with prompt versioning, evaluation and tracing to debug GenAI pipelines

- Proficiency with Computer Vision libraries and frameworks (e., OpenCV, TensorFlow, PyTorch, StableDiffusion) and experience in building real-world image/video analysis pipelines.

- Familiarity with Generative AI and LLMs, including experience building Agentic-RAG pipelines using frameworks such as ReAct with tool integrations, memory, and reflection

- Solid understanding of how to evaluate/test ML & AI models : accuracy, robustness, fairness, drift, latency, interpretability, and safety.

- Exposure in building ETL pipelines and advanced feature engineering workflows for ML/AI, including vision-based applications

- Strong foundation in statistics : regression, hypothesis testing, probability distributions

- Independent problem-solving skills, creativity, and the ability to prioritize in fast-paced environments.

- Strong collaboration and communication skills, with the ability to work effectively in global and virtual settings.

- Familiarity with cloud platforms (Azure preferred, including Azure AI Foundry, AzureML, OpenAI platform, Synapse and storage).

- Skilled in data visualization and BI tools (Power BI or equivalent), with the ability to present complex insights clearly to non-technical stakeholders.

- Agentic frameworks like lanGraph, AutoGen, CrewAI etc. familiar with implementing graph RAG, multimodal RAG