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.

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.

Offline/secure 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. Interested candidates can share their updated resume on [email protected]

📌 Senior Data science (Thane)
🏢 WonderBiz Technologies Pvt.
📍 Thane