Lead Consultant Data Scientist.

Inviting applications for the role of Principal Consultnt-Assistant Manager - Data Scientist.

In this role, The Data Scientist will apply advanced analytics, machine learning, and statistical modeling to generate actionable insights and solve complex business problems. The role involves data collection, cleaning, exploratory analysis, feature engineering, model development, and integration of AI/ML solutions into dashboards and applications. The Data Scientist collaborates with cross-functional teams to ensure models are aligned with business objectives, reproducible, and scalable.

This role supports initiatives such as demand forecasting, classification, workforce analytics. By leveraging predictive modelling, NLP, and AI-driven insights, the Data Scientist helps Client make data-driven decisions, optimize operations, and enhance customer and employee experiences across its multinational business functions.

Responsibilities

.Model Development & Statistical Analysis- Design, develop, and optimize machine learning, deep learning, and statistical models to solve complex business problems.

.GenAI & LLM Integration- Implement and integrate AI/ML models into GenAI and Agentic AI applications using LangChain, LangGraph, OpenAI APIs, RAG pipelines, and vector databases.

.End-to-End ML Pipelines- Build, deploy, and maintain scalable ML pipelines, from data ingestion and preprocessing to model deployment, monitoring, and lifecycle management.

.Production Deployment & MLOps- Deploy models as APIs or services using Docker, Kubernetes, FastAPI, and cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) while implementing CI/CD for automated testing and versioning.

.Performance Monitoring & Reliability- Monitor model performance in production, detect drift or degradation, and establish alerting mechanisms to ensure reliability, compliance, and governance.

.Analytics Integration - Embed models into real-time dashboards and analytics applications to deliver actionable insights for business and HR stakeholders.

.Governance & Compliance - Ensure model explainability (XAI), security, audit logging, and adherence to ethical, regulatory, and enterprise standards.

.Knowledge Sharing- Conduct and participate in training sessions, workshops, and technical discussions to share insights, AI/ML techniques, and best practices with team members.

.Stakeholder Communication - Translate complex model results, analytics, and AI-driven insights into clear, actionable recommendations for business and non-technical stakeholders.

.Documentation & Enablement - Create well-structured documentation, reproducible workflows, and explanatory reports to support model adoption, operational handovers, and team learning.

.Cross-Functional Collaboration - Works closely with senior data scientists, engineers, and product teams to implement AI/ML solutions, ensuring alignment with project requirements and business objectives.

.Independent Contribution - Executes tasks such as data cleaning, exploratory analysis, feature engineering, and baseline model development under guidance, contributing to end-to-end AI/ML workflows

.Stakeholder Alignment -Assists in presenting findings, visualizations, and insights to business and HR stakeholders, translating technical results into actionable recommendations.

Qualifications We Seek in You!

Minimum Qualifications / Skills

.Bachelor's or Master's degree in data science, Computer Science, Statistics, Mathematics, or a related quantitative field.

.Relevant year ofhands-on experience in data analysis, machine learning, or AI projects, preferably in corporate or academic settings.

.Certification in areas like GenAI, Databricks etc.

Preferred Qualifications / Skills

.Hands-on experience in data preprocessing, exploratory data analysis, and feature engineering to support ML and AI model development.

.Knowledge of machine learning, deep learning, and NLP techniques for classification, regression, clustering, and predictive modelling.

.Proficiency in Python, SQL, ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn), and familiarity with cloud platforms (AWS, Azure, GCP) and data visualization tools.

.Experience integrating models into dashboards, analytics tools, and prototype AI applications.

.Familiarity with MLOps practices, model monitoring, CI/CD workflows, and cloud platforms (AWS, Azure, GCP).

.Ability to translate business problems into data-driven solutions and communicate insights to stakeholders effectively.

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