Lead Data Specialist

Job Title - Lead Data Scientist - Level 9 - ACS Song

Management Level: Level 9 – Lead/Specialist

Location: Kochi

Must have skills: Data Science and Machine Learning

Good to have skills:

Experience: 5-8 years of experience is required

Educational Qualification: Graduation (Accurate educational details should capture)

Job Summary

We are seeking a Senior Data Scientist specializing in production-grade Machine Learning model development and deployment with 5+ years of experience in data science, machine learning, statistical modeling, and applied AI solutions. The role will focus on designing, developing, validating, and operationalizing machine learning models that solve real business problems and are deployed into production environments rather than limited proof-of-concept implementations. The ideal candidate should have hands-on experience with at least one major cloud platform such as Azure, GCP, or AWS, along with strong knowledge of Python, ML frameworks, model evaluation, feature engineering, MLOps, and production model monitoring. This position requires close collaboration with data engineers, ML engineers, architects, product teams, and business stakeholders to deliver scalable, reliable, explainable, and business-impact-driven ML solutions.

Roles and Responsibilities

• •Develop, validate, and deploy machine learning models that are production-ready and aligned with business objectives.
• •Translate business problems into analytical, statistical, and machine learning solutions with measurable outcomes.
• •Apply strong MLOps practices to ensure models are versioned, monitored, retrained, governed, and maintained after deployment.
• •Collaborate with engineering and business teams to move ML solutions from experimentation to scalable production implementation.
• •Identify business problems and design appropriate machine learning, statistical, or AI-based approaches to solve them.
• •Build, train, validate, and optimize machine learning models using production-ready data science and engineering practices.
• •Perform feature engineering, model selection, hyperparameter tuning, model evaluation, explainability analysis, and performance benchmarking.
• •Deploy ML models into production environments in collaboration with ML engineers, data engineers, DevOps teams, and cloud platform teams.
• •Monitor production models for performance, data drift, model drift, bias, latency, reliability, and business impact.
• •Work experience with cloud platforms such as Azure, GCP, or AWS for model development, training, deployment, experimentation, and monitoring.
• •Communicate model outcomes, limitations, risks, assumptions, and business value clearly to technical and non-technical stakeholders.

Professional and Technical Skills:

5+ years of professional experience in data science, machine learning, applied AI, statistical modeling, or related roles.

Hands-on experience developing and deploying ML models into production environments, not only proof-of-concept or experimental setups.

Experience working with cloud-based ML platforms or services on Azure, GCP, or AWS.

Proven experience applying machine learning to real-world business problems with measurable outcomes. Industry specialization in Retail, Ecommerce and Automotive is a plus.

Strong programming skills in Python, (or R) for data science, machine learning, experimentation, and production-oriented development.

Strong understanding of supervised learning, unsupervised learning, classification, regression, clustering, forecasting, anomaly detection, and model evaluation techniques.

Hands-on experience with ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, MLflow, or similar tools.

Good knowledge of SQL and experience working with structured, semi-structured, and large-scale datasets.

Experience with feature engineering, data preprocessing, model explainability, bias evaluation, and performance optimization.

Familiarity with cloud ML services such as Azure Machine Learning, GCP Vertex AI, AWS SageMaker, or equivalent platforms.

Experience with Git, experiment tracking, CI/CD concepts, model registries, containerization, logging, monitoring, and production release workflows.

Strong understanding of the end-to-end ML lifecycle from problem framing and experimentation to deployment and monitoring.

Experience with model versioning, experiment tracking, model registry, reproducible pipelines, and automated model evaluation.

Ability to design and support batch inference, real-time inference, scheduled retraining, and model performance monitoring.

Understanding of model governance, responsible AI, data privacy, explainability, auditability, and secure handling of model artifacts.

Ability to troubleshoot production issues related to model degradation, data quality, drift, latency, scalability, and prediction accuracy.

Additional Information

Behavioral and Collaboration Skills

• •Strong analytical, troubleshooting, and problem-solving skills.
• •Ability to work effectively with architects, product owners, data engineers, backend developers, DevOps teams, and business stakeholders.

Strong communication skills (English) with the ability to participate in technical discussions and explain implementation approaches clearly.

• •Proactive mindset with ownership of assigned features, production issues, experimentation, and continuous improvement.
• •Comfortable working in agile teams and participating in sprint planning, technical discussions, demos, code reviews, and implementation activities.

About Our Company | Accenture (do not remove the hyperlink)

• •1–2 years of hands-on experience in Agentic AI, LLM application development, AI agents, RAG-based solutions, GenAI workflow automation, or multi-agent systems.
• •Minimum 5 years of professional experience in backend development, data engineering, or a combination of both.
• •Experience building production-grade applications, APIs, data pipelines, automation workflows, enterprise integrations, or cloud-native services.
• •Hands-on experience or implementation exposure with MCP — Model Context Protocol for connecting AI agents or LLM applications with tools, APIs, enterprise systems, and external data sources.
• •Hands-on experience or working knowledge of A2A — Agent2Agent Protocol or similar agent interoperability patterns for enabling communication, coordination, and collaboration between AI agents.
• •Prior experience working in cloud-based environments and deploying scalable, reliable, and secure solutions
• •Hands-on experience with at least one major AI cloud platform: AWS Bedrock, Azure AI Foundry, or GCP Vertex AI.
• •Strong understanding of Agentic AI concepts such as tool calling, planning, reasoning, memory, multi-agent workflows, orchestration, autonomous task execution, and agentic workflow design.
• •Experience working with MCP clients, MCP servers, tool registration, tool execution, context retrieval, and secure integration of external systems with LLM applications.
• •Experience with A2A-based or multi-agent communication patterns, including agent discovery, capability exchange, task handoff, inter-agent messaging, and collaborative workflow execution.
• •Experience with LLM application frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar frameworks.
• •Strong programming skills in Python; experience with Java, Node.js, or other backend technologies is an added advantage.
• •Experience developing backend services, REST APIs, microservices, event-driven applications, or integration layers.
• •Good understanding of Retrieval-Augmented Generation, embeddings, vector search, semantic search, chunking strategies, document ingestion, and prompt engineering.
• •Familiarity with vector databases or search platforms such as Azure AI Search, Amazon OpenSearch, Pinecone, Weaviate, FAISS, Chroma, Milvus, or similar tools.
• •Experience with