Sr. Data Scientist

Location: Bengaluru, India

Department: Data Science

Experience: 8-12

Skills: AI ML Driven analysis, Data Analytics

• 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.

• 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 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.

• M.S. or B.Tech/B.E. in Computer Science, Statistics, Mathematics, or a related quantitative field or equivalent hands-on experience.
• 5–8 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.

• 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.