JOB DESCRIPTION

Job Title: Associate Consultant – Machine Learning & Data Science

Key accountabilities:

As an Machine Learning & Data Science Engineer, you will be accountable for:

Collaborating with specialized teams to understand operational needs and translating them into data science / machine learning technical problem statements with clearly defined success metrics and business impact.

Exploring structured and unstructured data to identify patterns and build models to learn these patterns using traditional statistical methods and machine learning models.

Demonstrate extensive experience across the full spectrum of Machine Learning modeling techniques, including supervised (classification, regression, time-to-event/ranking, multi-label), unsupervised (clustering, dimensionality reduction, association rule mining, anomaly detection), time series forecasting & deep learning (CNNs, RNNs/LSTMs/GRUs, transformers, autoencoders, graph neural networks) techniques along with the ensemble approaches such as bagging, boosting & stacking.

Performing in-depth exploratory data analysis (EDA) to identify important features, and executing advanced feature engineering and data transformation to create effective inputs for model training.

Selecting and evaluating relevant machine learning techniques through iterative experimentation, hyperparameter tuning, and robust validation, and deploying models in Azure using Databricks and MLflow.

Applying appropriate model evaluation metrics and validation approaches for various supervised and unsupervised problems, including baselining and statistical significance testing where relevant.

Presenting and promoting analytics solutions to stakeholders and relevant governance committees, translating technical results into actionable business insights and recommended actions.





Implementing and delivering end-to-end solutions through individual contributions and cross-team collaboration, documenting processes, methodologies, and findings via technical documentation, training sessions, and knowledge-sharing activities.

Demonstrating understanding of LLM and GenAI concepts, and identifying opportunities to apply them responsibly to business problems (e.g., prompt design, retrieval-augmented generation, evaluation of GenAI outputs).

Ensuring data governance, privacy, and security considerations are embedded in all analytics projects, including documentation of data lineage, access controls, and compliance with applicable regulations.

Establishing/participating in ongoing model monitoring and maintenance plans (drift detection, performance monitoring, retraining triggers, and incident response) to sustain business value from deployed models.

Technical and professional skills:

We are looking for a flexible, fast learning, technically solid engineer. Expertise is required in the following fields:

Strong knowledge and hands-on experience with machine learning algorithms across domains, including regression, classification, clustering, time series forecasting, and deep learning.

Proficiency in statistical concepts such as probability, hypothesis testing, linear models, time series analysis, and experimental design.

Strong programming skills in Python (including object-oriented programming) and software engineering practices (clean code, testing, debugging, documentation).





Experience with Azure Machine Learning and Databricks for model development, training, deployment, and collaboration; familiarity with Azure DevOps and MLflow for MLOps, CI/CD, and model/version tracking.

Expertise with Python data manipulation and visualization libraries (Pandas, NumPy, Matplotlib, Seaborn, Plotly) and distributed computing tools (PySpark, Dask) as well as scalable data processing workflows.

Experience with Generative AI technologies and frameworks (OpenAI, Azure OpenAI, LangChain) and practical knowledge of prompt design, evaluation, and integration into applications.

Experience with data engineering concepts and tools (ETL/ELT, data pipelines, feature stores) and the ability to collaborate with data engineers to productionize data workflows.

Familiarity with insurance-related problems and regulatory considerations is a strong plus (claims, fraud detection, risk scoring, pricing, customer value), plus awareness of data privacy and compliance requirements (e.g., GDPR).

Ability to design and implement model monitoring, logging, and governance controls to ensure reliability, fairness, and traceability.

Soft skills and competencies:

Good communication skills to coordinate between business stakeholders & engineers

Strong results-orientation and time management

True team player who is comfortable working in a global team

Ability to establish relationships with stakeholders quickly in order to collaborate on use cases

Autonomy, curiosity and innovation capability

Comfortable working in a multidisciplinary team within a fast-paced environment

Strong leadership skills to build and grow team and expand into newer applications

Background and experience:

4 to 6 years of hands-on professional experience in machine learning, big data and artificial intelligence.

📌 Associate Consultant (Pune)
🏢 AXA Global Business Services
📍 Pune