Sr.Data Scientist

EGIC 2.0 Transformation Context 

EGIC is undergoing a fundamental transformation from reactive operations (EGIC 1.0) to an autonomous intelligence engine (EGIC 2.0) through two strategic programs: 

• AIM (AI Integration & Modernization): Embedding AI across all EGIC workflows — automating reactive alarm triage, building predictive intelligence, and liberating 25–30 FTE-equivalent capacity through agentic automation. 

• APEX (Adoption, Proactive Engagement & eXpansion): Driving digital adoption, converting hardware-only customers to digital subscribers, and scaling TVD from $22M to $100M+ through proactive customer engagement. 

This role is central to both programs — the candidate will lead the AI team that powers AIM and provides the intelligence backbone for APEX. 

3. Role Summary 

3.1 The Opportunity 

We are looking for a Sr AI Engineer — a rare hybrid professional who combines deep business process understanding with advanced AI/Data Science technical expertise and proven people leadership. This individual will lead a team of 5–6 data scientists and AI engineers, driving the daily AI operations, project delivery, model deployment, adoption enablement, and production triage across EGIC's entire digital product portfolio. 

The ideal candidate has walked a deliberate career path: starting with understanding how business processes work (BPA), then building the AI that transforms those processes (Data Science), and now leading the team that operates, scales, and governs that AI at enterprise grade (Leadership). This combination ensures the candidate doesn't just build technically impressive models, but builds the RIGHT models that solve REAL operational problems and deliver MEASURABLE business outcomes. 

3.2 What Makes This Role Unique 

• You will lead AI operations for one of the largest industrial IoT intelligence centers in the world — 600K+ connected assets, 250B+ data points/year 

• Your models will directly impact water conservation, energy efficiency, and sustainability outcomes for customers across 40+ industries globally 

• You will have end-to-end ownership: from identifying the business problem (BPA lens) → designing the AI solution (DS lens) → deploying and scaling it (Ops lens) → measuring its impact (Leadership lens) 

• You will build and shape a team from a position of influence — defining the culture, technical standards, and the operational playbook 

• Direct visibility to EGIC Director and senior leadership — your work directly drives strategic decisions and commercial outcomes ($100M+ TVD target) 

• Cutting-edge technology: Databricks, Azure, LLMs, agentic AI, RAG pipelines, edge computing, real-time IoT streaming 

3.3 This Role Is NOT 

• NOT a pure research/academic role — we need production engineers, not just experimenters 

• NOT a people-only management role — you must be technically hands-on and review architecture decisions 

• NOT a single-project role — you will manage a portfolio of 3–5 concurrent AI projects across different digital products 

• NOT isolated from the business — you will regularly interact with Operations, Product, Engineering, Field, and Customer-facing teams 

4. Key Responsibilities 

4A. Business Process Analysis & Operations Leadership 

Your BPA foundation differentiates this role from a typical Data Science lead. You will use process analysis expertise to ensure every AI solution is grounded in a deep understanding of the operational workflow it serves. AI without process understanding leads to technically impressive but operationally useless solutions. 

i. End-to-End Process Mapping & Analysis 

• Map all EGIC operational workflows across 13 digital products — from data ingestion → alarm generation → triage → insight creation → customer delivery → value capture (TVD) 

• Identify human touchpoints, decision nodes, escalation paths, and handoff points in each workflow 

• Quantify time spent, error rates, and throughput at each process step to identify automation ROI 

• Use BPMN (Business Process Modeling Notation) or equivalent tools to create standardized, version-controlled process documentation 

ii. Automation Opportunity Identification 

• Systematically identify and prioritize automation candidates using a structured scoring framework (Impact × Feasibility × Urgency) 

• Distinguish between rule-based automation (RPA), ML-based automation (predictive models), and agentic automation (LLM-powered agents) — recommending the right approach for each use case 

• Build and maintain an "Automation Opportunity Backlog" — a prioritized pipeline of process improvements the AI team will deliver against 

• Conduct JTBD (Jobs To Be Done) analysis for each manual workflow to understand the root purpose before automating 

iii. SOP Design & Governance 

• Design and document Standard Operating Procedures (SOPs) for all AI-augmented workflows — covering normal operations, exception handling, escalation protocols, and fallback procedures 

• Ensure SOPs are practical, maintainable, and actually followed by operations teams — not just shelfware 

• Establish a quarterly SOP review cadence to incorporate lessons learned from production incidents and process changes 

iv. Operational KPI Framework 

Define, instrument, and track operational KPIs across all AI-augmented workflows: 

• SLA adherence rate (% of alarms/insights delivered within defined timeframes) 

• Automation rate (% of alarms handled without human intervention) 

• Triage turnaround time (mean time from alarm to resolution) 

• FTE liberation (hours/FTE equivalent freed through automation) 

• Model uptime (% availability of deployed AI models) 

• Inference latency (p50, p95, p99 response times for real-time models) 

• False positive/negative rates for alarm classification models 

• Build automated KPI dashboards (Power BI / Databricks) and publish weekly operational reports to leadership 

v. Cross-Functional Process Alignment 

• Facilitate process alignment workshops between Operations, Engineering, Product, and Field teams 

• Ensure AI outputs integrate seamlessly into existing operational tools (ServiceMax, ECOLAB3D, Salesforce) 

• Act as the "process conscience" of the AI team — ensuring solutions are designed for the real operational context 

vi. AIM Program Contribution 

• Contribute to EGIC's AIM (AI Integration & Modernization) program by identifying, chartering, and executing rationalization and automation projects 

• Lead process discovery sessions for new AIM workstreams — understanding current-state workflows before designing AI solutions 

• Track and report AIM impact metrics: FTE liberated, hours saved, error reduction, customer impact 

4B. AI / Data Science Technical Leadership 

This is where your Data Science expertise drives technical vision. You will own the architectural integrity of all AI solutions, ensuring they are not just accurate but production-grade, scalable, maintainable, and cost-efficient. You must be hands-on enough to review code and architecture — and strategic enough to choose the right battles. 

i. AI/ML Model Design & Development 

Lead the design, development, and deployment of ML/AI models for EGIC's core industrial IoT use cases: 

• Alarm Auto-Classification: Multi-class models that classify, prioritize, and route alarms across Water, Institutional, and Pest products 

• Predictive Maintenance: Time-series models predicting asset failures (pumps, controllers, sensors) before they impact operations 

• Anomaly Detection: Unsupervised models detecting novel patterns in industrial telemetry data (water quality, chemical dosing, temperature, conductivity) 

• NLP-Based Report Generation: LLM-powered systems auto-generating customer-facing reports, business reviews, and insight summaries 

• Agentic Automation: Multi-agent systems orchestrating complex operational workflows (alarm → analysis → recommendation → delivery) with minimal human intervention 

• TVD Discovery: AI models scanning connected assets to identify, quantify, and dollarize value opportunities for customers 

ii. Architecture Review & Governance 

• Review and approve ALL model architectures proposed by team members before development begins 

• Ensure correct architecture selection: batch vs. real-time inference, cloud vs. edge deployment, rule-based vs. ML vs. LLM-based approaches, monolithic vs. microservices model serving 

• Maintain an Architecture Decision Record (ADR) documenting rationale behind key technical choices 

• Conduct monthly "Architecture Review" sessions where the team presents and defends design decisions 

iii. GenAI, LLM & Agentic AI 

• Lead evaluation and integration of GenAI technologies: RAG pipelines, LLM-powered conversational agents, agentic frameworks (LangChain, LangGraph, CrewAI) 

• Establish prompt engineering standards, prompt versioning, and LLM evaluation frameworks (hallucination rate, relevance, groundedness, safety) 

• Ensure guardrails and governance for GenAI: compliance with Ecolab AI ethics, data privacy, and IP protections 

iv. Data Engineering Collaboration 

• Work with Data Engineers to ensure clean, reliable, timely data pipelines feeding ML models 

• Define data contracts between data engineering and data science teams — specifying schema, SLAs, and quality expectations 

• Oversee real-time streaming pipelines (Kafka / Azure Event Hubs) for time-sensitive use cases 

v. Innovation & Emerging Technology 

• Stay current with AI/ML research; evaluate emerging technologies (multimodal AI, Small Language Models, Graph Neural Networks, Reinforcement Learning, Federated Learning) 

• Run structured PoC → Pilot → Production cycles for promising technologies 

• Present quarterly "Technology Radar" updates to leadership highlighting emerging capabilities and EGIC relevance 

4C. Team Leadership & People Management 

You will build, lead, and develop a team of 5–6 data scientists and AI engineers. This is not just task allocation — it's about creating a high-performing team culture where technical excellence, ownership, and continuous learning are the norm. You are expected to be a player-coach. 

i. Team Building & Culture 

• Build a culture centered on: technical rigor, ownership, transparency, continuous learning, and collaborative problem-solving 

• Establish team rituals: daily standups, weekly technical deep-dives, monthly innovation hours, quarterly retrospectives 

• Create a psychologically safe environment for experimentation, fast failure, and improvement 

• Foster knowledge sharing through internal tech talks, code review sessions, and documentation standards 

ii. People Management 

• Conduct weekly/bi-weekly 1:1s — covering project progress, blockers, career aspirations, and wellbeing 

• Lead quarterly performance reviews aligned with Ecolab's VSEM framework 

• Create Individual Development Plans (IDPs) for each team member — mapping skill gaps, training, and career progression 

• Handle team dynamics, conflict resolution, and performance improvement with empathy and professionalism 

• Participate in hiring: interviews, candidate evaluation, and onboarding of new team members 

iii. Project & Delivery Management 

• Manage team capacity across 3–5 concurrent projects — balancing business priority, member growth, and delivery timelines 

• Run Agile/Scrum sprint cycles (2-week sprints) with planning, estimation, execution, and retrospective ceremonies 

• Maintain project portfolio dashboard: status, milestones, risks, dependencies, and resource allocation 

• Ensure every project has a clear charter: problem statement, success criteria, data requirements, architecture, timeline, and ownership 

iv. Upskilling & Professional Development 

• Drive structured upskilling: Databricks certifications, Azure certifications, LLM/GenAI training, MLOps best practices, soft skills development 

• Allocate 10% of sprint capacity for self-directed learning and experimentation 

• Encourage conference presentations, blog posts, and internal knowledge sharing 

4D. Deployment, Adoption & Production Triage 

A model that works in a notebook but fails in production is not a success. This section ensures every AI solution survives — and thrives — in the real operational environment. 

i. Production Deployment & Release Management 

Every deployment follows a standardized release checklist: 

• Code review completed and approved 

• Unit tests and integration tests passing 

• Performance benchmarks met (accuracy, latency, throughput) 

• Data quality checks validated 

• Rollback plan documented and tested 

• Monitoring and alerting configured 

• Stakeholder sign-off obtained 

• Implement blue-green or canary deployment strategies for high-risk model updates 

ii. Production Monitoring & Health 

• Build comprehensive monitoring dashboards: model performance, data quality, inference latency, business impact, infrastructure utilization 

• Configure automated alerting for performance degradation, data drift, concept drift, and infrastructure issues 

• Publish weekly "Production Health Report" summarizing model status, incidents, and actions 

iii. Incident Triage & Resolution 

Establish and manage a structured triage process: 

• Severity Classification: P1 (critical — customer impact, 30-min response), P2 (high — 2-hr response), P3 (medium — 1 business day), P4 (low — 1 week) 

• Mandatory Root Cause Analysis (RCA) for all P1 and P2 incidents with post-mortem documentation 

• Shared on-call rotation across 5–6 team members for after-hours P1/P2 incidents 

• Maintain incident knowledge base documenting past incidents, root causes, and resolutions 

iv. Adoption Enablement 

• Drive adoption of AI solutions across operations — ensure teams USE and TRUST AI outputs 

• Conduct training sessions and workshops for operations teams on interpreting and acting on AI insights 

• Create user guides, quick-reference cards, and FAQ documents for each deployed solution 

• Establish feedback loops: collect user feedback on accuracy / usability / trust; feed back into model improvement 

• Track adoption metrics: % of AI insights acted upon, user satisfaction, time-to-action 

v. Documentation & Knowledge Management 

• Maintain comprehensive documentation: model cards, API docs, data dictionaries, runbooks, architecture diagrams 

• Ensure documentation is current, searchable, and accessible to all relevant stakeholders 

5. Required Qualifications 

5.1 Education 

• B.E. / B.Tech in Computer Science, Data Science, Information Technology, Chemical Engineering, or related engineering discipline — Required 

• M.S. / M.Tech in Data Science, AI/ML, Statistics, Applied Mathematics, or Computational Engineering — Preferred 

• MBA or PG Diploma in Business Analytics / Operations Management — Strong plus for the BPA dimension 

Valued Certifications: 

• Databricks Certified Data Scientist / ML Professional 

• Azure Data Scientist Associate (DP-100) / Azure AI Engineer Associate (AI-102) 

• Lean Six Sigma Green/Black Belt 

• PMP / PMI-ACP / Certified Scrum Master 

5.2 Experience Breakdown 

The ideal candidate has a blended career trajectory spanning three distinct capability areas: 

5.4 Technical Skills — Nice to Have 

• Edge AI / IoT deployment (Raspberry Pi, NVIDIA Jetson, TensorFlow Lite, ONNX Runtime) 

• Computer Vision for industrial applications (defect detection, gauge reading, visual inspection) 

• Databricks Apps, Genie Spaces, AI/BI Dashboards 

• Microsoft Copilot Studio, Power Platform, Power Automate 

• Snowflake (EGIC uses both Databricks and Snowflake) 

• Kubernetes / Docker for containerized ML model deployments 

• Agent-to-Agent (A2A) and Model Context Protocol (MCP) architectures 

5.5 Domain Knowledge — Critical 

This role operates at the intersection of AI and industrial operations. Domain knowledge is not optional — it is what separates a good data scientist from one who can deliver real impact at EGIC. 

• Industrial Water Treatment: Cooling tower chemistry, boiler water management, RO/membrane systems, wastewater treatment, chemical dosing, corrosion/scale control. Understanding why parameters like conductivity, pH, ORP, turbidity, and cycles of concentration matter operationally. 

• IoT / Industrial Telemetry: Real-time sensor data from 3D TRASAR, AIO controllers, OMNI systems. Alarm management principles (ISA-18.2), sensor calibration and drift, SCADA-level concepts. Understanding data flow from field devices through gateways to cloud. 

• 24/7 Operations & Service Delivery: SLA-driven operations, shift-based monitoring, escalation protocols, incident management (ITIL-aligned). Understanding the operational tempo and pressure of a global intelligence center. 

• Industry Preference: Experience at Nalco Water, Ecolab, Veolia, Suez, Xylem, Kemira, BASF, Dow, Honeywell Process Solutions, Emerson, or Schneider Electric is highly preferred. 

• Platform Familiarity: ECOLAB3D, ServiceMax, Salesforce, or equivalent industrial IoT and field service platforms. 

5.6 Soft Skills & Competencies 

• Leadership & Influence: Lead without authority, influence cross-functional stakeholders, drive alignment. Comfortable presenting to Director-level and above. 

• Communication Excellence: Explain complex AI/ML concepts to non-technical audiences. Strong written communication for docs, reports, and executive summaries. 

• Process-Oriented Mindset: Natural inclination to understand, document, and improve processes. Bias for systematic approaches over ad-hoc solutions. 

• Bias for Action: Make decisions with imperfect information. Preference for "good enough now" over "perfect later" — especially in production triage. 

• Problem-Solving Under Pressure: Calm, structured, time-sensitive decision-making in production environments. 

• Cross-Functional Fluency: Fluent in technology, operations, and business languages. Can translate between all three seamlessly. 

• Growth Mindset: Genuine curiosity about new technologies, willingness to learn, adapt, and teach others.