Responsibilities
Lead data migration activities and analysis
Work with Data scientists/Business Analysts/Product Owner to understand MI and dashboard requirements
Design Data Solutions: Leverage your analytical skills to design innovative data solutions that address complex business requirements and drive decision-making
Create detailed field level mappings as needed for different MI Dashboards
Develop and maintain comprehensive dashboards to visualize key performance indicators
Conduct market research and trend analysis to inform strategic business decisions
Collaborate with cross-functional teams to ensure data accuracy and integrity and recommend process improvements based on data analysis findings
Prepare detailed reports and presentations on analytical findings for stakeholders and stay updated on industry trends, data analysis techniques, and best practices
Requirements
Bachelor's degree in computer science, Information Technology, or a related field; advanced degree preferred
Minimum of 8+ years of experience in data engineering or a similar role
Experience in the Pydata stack: Pandas, Numpy
Knowledge of data modelling in Python and Excel
Strong proficiency in statistical analysis software and data visualization tools such as Tableau, Power BI, or similar
Knowledge of relational database technologies such as Teradata, Oracle and Microsoft SQL with strong SQL execution and optimisation skills
Excellent documentation, report writing & presentation skills
Advanced Technical & AI Capabilities
Technical Proficiency: Strong proficiency in Python for data analysis and software engineering, including experience with libraries like PyTorch or TensorFlow
AI Expertise: Hands‑on experience with LLMs, RAG (Retrieval‑Augmented Generation) systems, vector databases, and prompt management
Machine Learning Fundamentals: Knowledge of classical ML algorithms, data preprocessing, and model evaluation techniques
Frameworks: Experience with agentic libraries (e.g., LangChain, AutoGen, CrewAI)
Cloud & DevOps: Experience with deploying models on cloud platforms (e.g., AWS, GCP, Azure) and using MLOps tools (e.g., Docker)
#J-18808-Ljbffr