We're hiring a Leader for our Data Science (DS) team to deliver production-grade ML for industrial systems using time-series and multimodal data (sensor/SCADA/historian, events, maintenance logs, and images/video where relevant).

This is a hands-on role where you'll spend most of your time building and shipping (roughly 70% IC), while also leading and growing the team (roughly 30% people leadership).

You'll work closely with customers and domain SMEs, ship models to production,and evolve our concept from "models" to an autonomous decisioning system: forecasting - detection/diagnosis - optimization - closed-loop actions (with safety + governance).

A key part of the role is advancing our unique IP in closed-loop autonomous operations.

What You'll Do :

- Own the end-to-end lifecycle: problem framing - data readiness - modeling - deployment - monitoring - iteration.

- Define and execute roadmap areas like anomaly/event detection, asset/process health, root-cause support, optimization, and closed-loop decision support.

- Build scalable foundations for baselines, drift detection, model observability, and incident response.

- Partner with industrial customers and SMEs to translate real process constraints into ML/optimization/decisioning solutions.

- Drive unsupervised/self-supervised initiatives (representations, clustering, change-point detection, weak supervision, active learning).

- Develop a practical Reinforcement Learning (RL)/decisioning strategy (offline/safe RL, constrained optimization, simulators/digital twins), with guarded rollout patterns.

- Lead and mentor DS talent, set processes, frameworks and quality standards (design/code reviews, documentation, postmortems).

- Build and deploy AI / ML solutions / models in production.

- Own deployment, monitoring, performance validation, and iteration of models in production.

- Identify, document, and progress patentable innovations tied to closed-loop autonomy and production deployment.

Qualifications :

- 8+ years in applied AI / ML technologies, including 2+ years leading teams (hiring, mentorship, performance management).

- Deep experience with time-series ML at scale, ideally with messy industrial data [Ex: Frequency-domain time-series techniques (FFT/spectral analysis) and control/optimization methods (MPC-like approaches)].

- Proven track record of shipping and operating AI / ML solutions in production (MLOps, monitoring, drift, retraining, reliability).

- Strong Python and engineering fundamentals (clean code, testing, production patterns).

- Strong communication, comfortable working directly with customers and cross-functional teams.

Bonus Points :

- Offline/safe RL, constrained optimization, and/or simulators/digital twins.

- Self-supervised learning or foundation-model approaches for industrial time-series and multimodal fusion.

- Robotics and / or Industrial domain experience (manufacturing, energy, chemicals, mining, utilities), including safety/uptime/latency/edge constraints.

- Closed-loop or human-in-the-loop decision systems with governance and guardrails.

- Experience contributing to IP strategy, invention disclosures, and patent filings.

Education :

- UG: B.Tech/B.E. in Any Specialization.