Data Scientist, D&T

Job Summary

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.g., 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.g., 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.g., 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

License and Certifications

Travel Requirements

Physical Requirements

Additional Requirements