Senior Data Scientist

Skan AI; Bengaluru; Hybrid; INR 400,000 - 800,000; Full time

Job summary

Skan AI is seeking a Senior Data Scientist to build and own ML models and data pipelines, handling large, messy real-world datasets. You will guide junior data scientists and analysts, providing day-to-day technical direction and code reviews.

You are not a manager, but a multiplier, delivering hands-on modelling, debugging customer data problems, and collaborating with cross-functional teams to drive enterprise-ready AI solutions.

Qualifications

• M.S. or B.Tech/B.E. in Computer Science, Statistics, Mathematics, or a related quantitative field or equivalent hands-on experience.
• 7–12 years of industry experience building and shipping data science or ML solutions in production.
• Strong Python skills with clean, modular, testable code as a baseline expectation.
• Solid experience with large-scale data processing using Spark, Databricks, or similar distributed frameworks.
• Proficiency in at least two of: supervised/unsupervised ML, NLP, deep learning, sequence modelling, or process mining.
• Experience diagnosing and fixing real-world data quality and model behaviour issues in production systems.
• A genuine interest in helping junior colleagues grow; this should excite you, not feel like a tax on your time.
• Clear, concise communication: you can explain a complex model or a data issue to an engineer, a PM, or a customer success manager without jargon.

Responsibilities

• Build, train, evaluate, and improve ML models that power core Skan.ai features including task detection, workflow segmentation, behavioral pattern recognition, process discovery, and variant analysis.
• Own the full modelling lifecycle: data exploration, feature engineering, model selection, hyperparameter tuning and validation.
• Run disciplined experiments: define clear hypotheses, design evaluation frameworks, and present findings with statistical rigour.
• Explore and apply techniques from deep learning, NLP, time-series analysis, and unsupervised learning to real process intelligence problems.
• Write clean, well-tested, production-ready Python code that your teammates can maintain and build on.
• Design and build reliable data pipelines that handle high volumes of multimodal enterprise data such as screen telemetry, event logs, clickstreams, and structured process data.
• Work hands-on with distributed data platforms (Spark, Databricks, or equivalent) to process, transform, and validate data at scale.
• Own data quality at every step: define validation checks, detect drift, monitor pipeline health, and fix issues proactively.
• Collaborate with engineering to optimise ingestion and feature generation workflows for speed, cost, and reliability.
• Investigate and resolve data and model issues that surface in customer environments; trace root causes across pipelines, features, and model behaviour.
• Partner with Customer Success and Solutions teams to understand unexpected outcomes and translate them into technical fixes.
• Build internal diagnostic tooling and dashboards that make it easier to spot and triage data anomalies and model degradation in production.
• Document findings clearly so that patterns are captured and future issues are resolved faster.
• Serve as the day-to-day technical guide for junior data scientists and analysts; answer questions, review code, pair on hard problems, and help them grow.
• Conduct thorough, constructive code reviews that improve both the work and the person who wrote it.
• Help junior teammates develop good habits: experiment tracking, reproducibility, clean data handling, and robust validation.
• Run informal knowledge-sharing sessions: walkthroughs of new techniques, post-mortems on what went wrong, or deep-dives into a dataset.
• Flag blockers and skill gaps to the Principal/Chief Scientist and help shape how the team grows technically over time.
• Work closely with product and engineering to understand requirements, scope data work, and deliver on time.
• Communicate findings clearly to non-technical stakeholders; translate model outputs and data insights into plain language that drives decisions.
• Contribute technical input to sprint planning and quarterly priorities; flag feasibility concerns early and propose alternatives.

Skills

Python

Education

M.S. or B.Tech/B.E. in CS/Statistics/Math

Tools

Spark

Databricks

Job description

Be at the Forefront of the Agentic AI Revolution

At Skan AI, we are pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work.

We're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes.

Backed by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises.

Our diverse, collaborative team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself.

The Role

We are looking for a Senior Data Scientist who loves to get their hands dirty. This is a deeply hands-on role; you will build and own ML models and data pipelines, work directly with large and messy real-world datasets, and be the go-to person when something breaks or doesn't make sense. You will also play a key guiding role for junior data scientists and analysts on the team, helping them grow through close collaboration, code reviews, and day-to-day technical direction.

You are not a manager, but you are a multiplier. The best candidate is someone who writes excellent code in the morning, helps a junior teammate unblock an issue in the afternoon, and debugs a tricky customer data problem before EOD, and finds all three equally satisfying.

What You'll Do

Hands-On Modelling & Algorithm Development

• Build, train, evaluate, and improve ML models that power core Skan.ai features including but not limited to task detection, workflow segmentation, behavioral pattern recognition, process discovery, and variant analysis.
• Own the full modelling lifecycle: data exploration, feature engineering, model selection, hyperparameter tuning and validation.
• Run disciplined experiments: define clear hypotheses, design evaluation frameworks, and present findings with statistical rigour.
• Explore and apply techniques from deep learning, NLP, time-series analysis, and unsupervised learning to real process intelligence problems.
• Write clean, well-tested, production-ready Python code that your teammates can maintain and build on.
• Design and build reliable data pipelines that handle high volumes of multimodal enterprise data such as screen telemetry, event logs, clickstreams, and structured process data.
• Work hands-on with distributed data platforms (Spark, Databricks, or equivalent) to process, transform, and validate data at scale.
• Own data quality at every step: define validation checks, detect drift, monitor pipeline health, and fix issues proactively.
• Collaborate with engineering to optimise ingestion and feature generation workflows for speed, cost, and reliability.

Customer Issue Diagnosis & Production Support

• Investigate and resolve data and model issues that surface in customer environments; trace root causes across pipelines, features, and model behaviour.
• Partner with Customer Success and Solutions teams to understand unexpected outcomes and translate them into concrete technical fixes.
• Build internal diagnostic tooling and dashboards that make it easier to spot and triage data anomalies and model degradation in production.
• Document findings clearly so that patterns are captured and future issues is resolved faster.
• Serve as the day-to-day technical guide for junior data scientists and analysts; answer questions, review code, pair on hard problems, and help them grow.
• Conduct thorough, constructive code reviews that improve both the work and the person who wrote it.
• Help junior teammates develop good habits: experiment tracking, reproducibility, clean data handling, and robust validation.
• Run informal knowledge-sharing sessions: walkthroughs of new techniques, post-mortems on what went wrong, or deep-dives into a dataset.
• Flag blockers and skill gaps to the Principal/Chief Scientist and help shape how the team grows technically over time.

Cross-Functional Collaboration

• Work closely with product and engineering to understand requirements, scope data work, and deliver on time.
• Communicate findings clearly to non-technical stakeholders; translate model outputs and data insights into plain language that drives decisions.
• Contribute technical input to sprint planning and quarterly priorities; flag feasibility concerns early and propose alternatives.

What We're Looking For

Required

• M.S. or B.Tech/B.E. in Computer Science, Statistics, Mathematics, or a related quantitative field or equivalent hands-on experience.
• 7–12 years of industry experience building and shipping data science or ML solutions in production.
• Strong Python skills with clean, modular, testable code as a baseline expectation.
• Solid experience with large-scale data processing using Spark, Databricks, or similar distributed frameworks.
• Proficiency in at least two of: supervised/unsupervised ML, NLP, deep learning, sequence modelling, or process mining.
• Experience diagnosing and fixing real-world data quality and model behaviour issues in production systems.
• A genuine interest in helping junior colleagues grow; this should excite you, not feel like a tax on your time.
• Clear, concise communication: you can explain a complex model or a data issue to an engineer, a PM, or a customer success manager without jargon.

Nice to Have

• Exposure to process mining, RPA, workflow analytics, or enterprise operations intelligence.
• Experience with MLflow, Weights & Biases, or similar experiment tracking and model management tools.
• Familiarity with LLMs, transformer-based models, or multimodal learning pipelines.
• Prior experience in a product-focused startup or scale-up environment.

Why Skan.ai

Not a support role: Real ownership from day one

• You will own models and pipelines end-to-end — not hand off tickets to a team in another timezone.
• Process intelligence is a frontier domain — you will work on problems that don't have off-the-shelf solutions.
• Join a high-calibre local team with direct access to our global AI research leadership.

Rewards: Competitive compensation

• Top-of-market salary for the Bengaluru market, performance-linked bonus, and a comprehensive benefits package.

This is a hybrid role based in Bangalore, with an expectation to work from the office three days a week.