Senior Data Scientist (M.Sc Statistics) at Fire AI
Hyderabad, Telangana, India · noida · full time · senior level
Compensation: INR 4000000 per year
About the role
## About the role Fire AI is hiring a Senior Data Scientist in Hyderabad — a statistician first, with an M.Sc in Statistics — to own the statistical core of its forecasting, anomaly detection and causal-chain analysis: the models that tell enterprises what will happen and why it happened. **Openings:** 1 · **Experience:** 4+ years · **Education:** M.Sc Statistics · **Employment:** full-time **Location:** Hyderabad, Telangana, India — new office in Financial District / HITEC City · **Working week:** 6 days — Monday to Friday from the office, Saturday from home · **Hours:** 10 AM – 7 PM · **Until the office opens (around 15 December):** work from home ## About Fire AI Fire AI is building a decision intelligence platform — not another BI tool. Intelligent dashboards, automated reporting, smart alerts in 60 seconds instead of 6 days, time-series forecasting at 94% accuracy, text-to-SQL at 98% accuracy and causal-chain analysis that answers "why", not just "what". 72% of its users are non-technical (CFOs, ops managers) working through conversational AI in 90+ languages. It serves 120+ brands across 17+ industries — including IRCTC, CWC, Faballey and GSN Group — with zero churn, grew from $28.3K to $512.9K MRR in seven months, and is backed by Venture Catalysts, IPV and SucSEED. The engineering team is expanding into a new Hyderabad office. ## What you will do - Own the statistical methodology behind time-series forecasting, anomaly detection and causal-chain ("why did this happen") analysis. - Build and validate forecasting models — classical (ARIMA/SARIMA, exponential smoothing, state-space) and ML-based — and know when each is right. - Apply causal inference — experiment design, A/B testing, difference-in-differences, regression discontinuity, uplift and propensity methods — to turn correlations into defensible explanations. - Design evaluation: backtesting, confidence intervals, calibration and accuracy SLAs that hold up across 120+ brands and 17+ industries. - Work with AI/ML engineers to take models into production, and with Product to explain results clearly to non-technical users such as CFOs and ops managers. ## What you need - M.Sc in Statistics (or Statistics-major equivalent) — required by the client - 4+ years as a data scientist applying statistics to real business problems - Strong time-series forecasting experience, with models that were used in decisions - Hands-on causal inference and experimentation (A/B tests, quasi-experimental methods) - Python (or R) and SQL; comfortable taking work toward production with engineers ## Nice to have - Bayesian methods and hierarchical models. - Anomaly detection on business or operational metrics. - Experience in analytics, BI or decision-intelligence products. **Interview process:** 3 rounds. ## Working model - **Location:** Hyderabad, Telangana. Fire AI is opening a new office in Financial District / HITEC City. - **Six-day working week:** Monday to Friday from the office, Saturday from home. - **Hours:** 10 AM – 7 PM. - **Before the office opens:** the office is expected to be ready around 15 December; until then the team works from home. - **Not a hybrid role.** After the office opens, Monday to Friday in the office is required. - **Relocation:** candidates outside Hyderabad need to relocate.
Responsibilities
- Own the statistical methodology behind time-series forecasting, anomaly detection and causal-chain ("why did this happen") analysis.
- Build and validate forecasting models — classical (ARIMA/SARIMA, exponential smoothing, state-space) and ML-based — and know when each is right.
- Apply causal inference — experiment design, A/B testing, difference-in-differences, regression discontinuity, uplift and propensity methods — to turn correlations into defensible explanations.
- Design evaluation: backtesting, confidence intervals, calibration and accuracy SLAs that hold up across 120+ brands and 17+ industries.
- Work with AI/ML engineers to take models into production, and with Product to explain results clearly to non-technical users such as CFOs and ops managers.
Requirements
- M.Sc in Statistics (or Statistics-major equivalent) — required by the client
- 4+ years as a data scientist applying statistics to real business problems
- Strong time-series forecasting experience, with models that were used in decisions
- Hands-on causal inference and experimentation (A/B tests, quasi-experimental methods)
- Python (or R) and SQL; comfortable taking work toward production with engineers
Skills: Statistics, Time-series forecasting, Causal inference, A/B testing, Experiment design, ARIMA, Bayesian methods, Anomaly detection, Python, R, SQL
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