Data Scientist

Data Scientist

Responsibilities:

• Leverage strong machine learning and data analysis experience in complex data environments to support business objectives in effective and scalable ways.

• Utilize a solid foundation in mathematics and statistics to guide model development, validation, and performance evaluation.

• Design and develop machine learning and LLM-based solutions, including applications such as RAG pipelines, chatbots, and intelligent assistants where applicable.

• Develop and integrate REST APIs and FastAPI-based services for model deployment and application integration.

• Support implementation of monitoring and observability practices for ML and LLM systems, including performance tracking and response evaluation.

• Build and maintain data pipelines, including data ingestion, preprocessing, transformation, and validation.

• Collaborate effectively within Agile Scrum teams, contributing to iterative development and continuous improvement.

• Document business processes, workflows, models, APIs, and technical implementations clearly and comprehensively.

• Engage with business and technical stakeholders to understand requirements and translate them into data science and AI solutions.

• Participate in collaborative conceptualization sessions to brainstorm and refine ML and LLM-based solutions.

• Assist in implementing agent-based workflows using modern frameworks and support integration with enterprise systems.

Mandatory Skills:

• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.

• Experience with data processing, cleansing, and validation to ensure data quality and integrity for analysis.

• Conduct data quality checks and exploratory data analysis to support model development.

• Demonstrated programming skills in Python and experience in building and integrating APIs.

• Build end-to-end machine learning models, including data preparation, feature engineering, and deployment workflows.

• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.

• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic approaches.

• Basic experience with Large Language Models such as GPT, BERT, or similar architectures.

• Understanding of prompt engineering and LLM application usage.

• Familiarity with vector databases, embeddings, and retrieval-based approaches.

• Basic understanding of agent frameworks such as LangChain or Semantic Kernel.

• Knowledge of data mining concepts and experience with data visualization tools and dashboards.

Preferred Skills:

• Experience working with Large Language Models and building simple LLM-based applications.

• Understanding of natural language processing (NLP) techniques and their application in business contexts.

• Familiarity with agent-based workflows and orchestration concepts.

• Basic knowledge of Model Context Protocol (MCP) or similar agent communication standards.

• Exposure to monitoring and observability practices for ML and LLM systems.

• Familiarity with RAG architectures and document retrieval pipelines.

• Exposure to advanced topics such as deep learning, transformers, and fine-tuning approaches.

• Ability to integrate ML and LLM solutions into larger product or business workflows.

• Identify opportunities to automate data processing, analysis, and business workflows using ML and LLM techniques.

• Propose hypotheses and conduct experiments to solve business problems using data-driven approaches.

Additional Responsibilities:

• Stay updated with the latest advancements in machine learning, LLMs, and AI tools.

• Contribute to team knowledge sharing and adoption of best practices in ML and LLM development.

• Support senior team members in building scalable, reliable, and efficient AI systems.

• Ensure data quality, consistency, and reliability across all data science workflows.

• Follow responsible AI practices, including fairness, explainability, and compliance standards.

Lead Machine Learning Engineer / Machine Learning Engineer

Responsibilities

• Lead and own theend‐to‐end production lifecycle of ML and LLM models (must have), ensuring models are deployable, scalable, observable, and maintainable.
• Define and enforceML engineering and MLOps standards across teams (must have).
• Design and maintainCI/CD pipelines for ML workloads (must have).
• Act astechnical lead and mentor for ML engineers and contributors (must have).
• Partner with Data Scientists toindustrialize research into production systems (must have).
• Collaborate with Platform, Cloud, and Data Engineering teams oninfrastructure and runtime alignment (must have).
• Ownmodel monitoring, drift detection, testing, rollback, and incident analysis (must have).
• Evaluate and introducenew ML, GenAI, and MLOps tools with a pragmatic, enterprise mindset (good to have).
• Contribute toML governance, reproducibility, and responsible AI practices (good to have).

Key Skills & Expertise

• Cloud & DevOps: Azure (must have), CI/CD using Jenkins, GitHub Actions, ArgoCD (must have)
• Container & Orchestration: Docker, Kubernetes (must have)
• Workflow Orchestration: Airflow (must have)
• Programming: Production‐grade Python (must have)
• ML Engineering & GenAI: LLM integration, prompt engineering, model packaging and lifecycle management (must have)
• Testing & Quality: Pytest, integration and system testing for ML systems (must have)
• Data: SQL, relational databases, basic reporting and dashboards (must have)
• ML Platforms: MLflow, Databricks (good to have)
• LLM Frameworks: LangChain, LangGraph, agent‐based patterns (good to have)
• Data Science Awareness: ML algorithms, feature engineering, evaluation metrics, bias/leakage awareness (awareness required)
• Specialized Use Cases: OCR and document processing pipelines (good to have)
• Frontend / Visualization: Streamlit, widgets, lightweight UI layers (good to have)

Mindset: Awareness of emerging technologies and new tooling (good to have)