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AGM Data Analytics, AI and Automation

DS Group
11 days ago
Full-time
On-site
Noida, Uttar Pradesh, India

Role Purpose

The role is responsible for leading the organization’s Artificial Intelligence, Machine Learning, Generative AI, Data Analytics, RPA, and advanced technology initiatives. The position will identify, design, implement, and scale AI-led solutions that improve business efficiency, decision-making, automation, productivity, and innovation across the organization.

The role will act as a bridge between business problems and AI-enabled solutions by working with business teams, IT teams, vendors, and senior leadership.

Key Responsibilities

1. AI/ML Strategy & Use Case Development

  • Lead identification of AI/ML use cases across business functions such as HR, Sales, Marketing, Finance, Supply Chain, Manufacturing, Customer Service, Legal, and IT.
  • Evaluate business processes to identify opportunities where AI, ML, NLP, computer vision, predictive analytics, and generative AI can create measurable impact.
  • Convert business problems into AI/ML solution requirements.
  • Prepare AI/ML implementation roadmaps with clear timelines, effort estimates, cost estimates, and expected ROI.
  • Prioritize AI/ML initiatives based on business value, feasibility, data availability, risk, and scalability.
  • Build a structured AI adoption framework for the organization.

2. Machine Learning Model Development & Deployment

  • Lead the development and deployment of machine learning models for prediction, classification, clustering, recommendation, forecasting, anomaly detection, and optimization.
  • Guide the team in data preparation, feature engineering, model training, testing, validation, and deployment.
  • Work on practical AI/ML use cases such as:
    • Demand forecasting
    • Attrition prediction
    • Sales prediction
    • Customer segmentation
    • Internal knowledge search
    • HR policy chatbot
    • Resume and JD matching
    • Automated report generation
  • Ensure models are accurate, explainable, scalable, and aligned with business requirements.
  • Monitor model performance, accuracy, bias, drift, and real-world business impact after deployment.


3. AI Solution Architecture & Integration and Governance

  • Design AI/ML solution architecture involving data sources, APIs, cloud platforms, data warehouses, applications, and user interfaces.
  • Integrate AI/ML models with enterprise systems such as ERP, HRMS, CRM, SAP, Oracle, PeopleStrong, Snowflake, Tableau, and internal applications.
  • Work with IT infrastructure, cybersecurity, and application teams to ensure secure deployment.
  • Support deployment of AI models through APIs, dashboards, bots, workflows, and business applications.
  • Guide the use of MLOps practices for versioning, deployment, monitoring, and lifecycle management.
  • Define governance standards for AI/ML implementation.
  • Ensure compliance with data privacy, information security, audit, and regulatory requirements.
  • Maintain documentation for datasets, model logic, assumptions, limitations, validation results, and user guidelines.


4. Data Analytics & AI-Driven Insights

  • Lead data analytics initiatives that support AI/ML model development and business decision-making.
  • Build dashboards and analytical reports using Tableau, Power BI, SQL, Snowflake, Python, or similar tools.
  • Use data analytics to identify patterns, trends, exceptions, risks, and opportunities.
  • Ensure clean, structured, and reliable data pipelines for AI/ML projects.
  • Work with business teams to define KPIs and convert data into actionable insights.
  • Strengthen data quality, master data governance, and reporting standardization.

5. RPA & Intelligent Automation

  • Identify processes where RPA can be enhanced with AI/ML, OCR, NLP, and decision intelligence.
  • Lead intelligent automation projects beyond basic rule-based automation.
  • Develop automation solutions for repetitive, document-heavy, and approval-based workflows.
  • Work on use cases such as invoice processing, data entry automation, employee onboarding workflows, document validation, report generation, and exception handling.
  • Monitor bot performance, automation success rate, exception rate, and time saved.
  • Ensure automation projects deliver measurable business value and ROI.

6. Advanced Technology & Innovation

  • Evaluate new technologies such as generative AI, agentic AI, computer vision, OCR, document AI, voice AI, IoT analytics, API automation, cloud AI services, and workflow intelligence.
  • Build proof of concepts for emerging technology solutions.
  • Convert successful pilots into scalable production-grade solutions.
  • Track industry trends and recommend relevant technologies for business adoption.
  • Create internal innovation pipelines for AI and automation use cases.
  • Develop reusable AI frameworks, templates, and components for faster implementation.

7. Project Management & Business Delivery

  • Lead end-to-end delivery of AI/ML, analytics, RPA, and advanced technology projects.
  • Prepare project plans, BRDs, solution documents, timelines, milestones, risk logs, and implementation trackers.
  • Coordinate with internal stakeholders, vendors, consultants, and technology partners.
  • Track business benefits after implementation, including cost savings, time savings, productivity improvement, accuracy improvement, and adoption.
  • Present project updates, business cases, ROI analysis, and performance reports to senior leadership.

8. Team Leadership & Capability Building

  • Lead a team of AI/ML engineers, data analysts, RPA developers, automation specialists, and technology associates.
  • Assign work, review technical deliverables, and ensure quality of solutions.
  • Mentor the team on AI/ML concepts, analytics, automation, solution design, and business communication.
  • Build internal capability through training, documentation, demos, and knowledge-sharing sessions.
  • Define team KRAs, KPIs, delivery standards, and innovation goals.
  • Promote a business-first approach to technology implementation.

Required Skills

AI/ML Skills

  • Strong understanding of machine learning algorithms and practical business applications.
  • Knowledge of supervised learning, unsupervised learning, classification, regression, clustering, forecasting, recommendation systems, anomaly detection, and optimization.
  • Understanding of model lifecycle: data preparation, feature engineering, training, testing, deployment, monitoring, and retraining.
  • Understanding of LLMs, prompts, embeddings, vector databases, RAG, AI agents, and enterprise copilots.
  • Ability to design AI chatbots, document search systems, summarization tools, and knowledge assistants.
  • Knowledge of prompt engineering, hallucination control, guardrails, access control, and evaluation of AI outputs.
  • Exposure to OpenAI, Azure OpenAI, Gemini, Claude, Hugging Face, LangChain, LlamaIndex, or similar platforms will be preferred.

Data & Analytics Skills

  • Strong SQL and data analysis skills.
  • Exposure to data warehouses and analytics platforms such as Snowflake, Databricks, BigQuery, Redshift, Oracle, or PostgreSQL.
  • Experience with BI tools such as Tableau, Power BI, Qlik, or similar.
  • Understanding of data pipelines, ETL/ELT, data quality, data governance, and master data management.

RPA & Automation Skills

  • Knowledge of RPA tools such as UiPath, Automation Anywhere, Power Automate, or similar.
  • Understanding of process automation, workflow automation, OCR, document processing, and exception handling.
  • Ability to calculate automation ROI based on time saved, cost saved, error reduction, and productivity improvement.

Managerial Skills

  • Strong project management and execution capability.
  • Team handling and technical review skills.
  • Stakeholder management across business and technology teams.
  • Vendor management and technology evaluation.
  • Ability to prepare business cases, ROI models, and leadership presentations.
  • Strong documentation and communication skills.

Qualification

  • Required: Bachelor’s degree in Engineering Computer Science, Electronics and Communication, Information Technology, or related field.
  • Postgraduate degree in Data Science, AI/ML, Business Analytics, or Digital Transformation will be preferred.

Experience

10–15 years of experience in AI/ML, data analytics, automation, digital transformation, or enterprise technology roles.