Senior Data Scientist

Fire AI | Hyderabad | 6–10 Years Experience

Would you rather build reports people skim, or models that tell a CFO why revenue dropped and what to do next?

Fire AI isn't another BI tool. We're the hypergrowth startup building the world's best decision intelligence platform with zero churn, 120+ category-defining brands (IRCTC, CWC, Faballey, GSN Group), and scaling from $28.3K to $16M MRR by 2030 with aggressive global expansion underway.

We're solving the enterprise analytics crisis where 92% of companies struggle with data analysis and 70% say static dashboards don't improve decisions. Our platform delivers intelligent dashboards, automated reporting, smart alerts (60 seconds vs. 6 days), time series forecasting (94% accuracy), and causal chain analysis that answers "why" not just "what." 72% of our users are non-technical (CFOs, ops managers) using conversational AI in 90+ languages.

Customer quote: "Feels less like a tool, more like a decision partner". We've driven 30% lower losses for pharma clients and 12% margin recovery for industry giants. Backed by Venture Catalysts, IPV, SucSEED.

What You'll Actually Own

You're not producing one-off analyses—you're defining the science behind forecasting, causal inference, anomaly detection, simulations, and AI-driven insights that enterprises act on every day.

- Own the modeling science end to end—frame ambiguous business problems, choose the right approach (statistical, ML, simulation, or LLM-based), build and validate models, and partner with ML Engineers to take them to production
- Lead forecasting, causal, and anomaly detection work—improve time series forecasting across diverse industries and data quality levels, build causal chain models that explain metric movements (root-cause analysis, driver decomposition, counterfactuals), and design anomaly detection that catches what matters without alert fatigue
- Build simulation environments and digital twins—model how a customer's business actually behaves (supply chains, inventory, pricing, demand, operations) so leaders can run "what-if" scenarios, stress-test decisions, and visualize outcomes before committing; combine simulation with forecasting and causal models to move from "what happened" to "what will happen if we do X"
- Drive experimentation and measurement—design A/B tests and quasi-experiments, define success metrics for AI features, and build evaluation frameworks proving models improve customer decisions




- Shape LLM and agentic insight features—work with Senior AI Engineers on agentic analytics, RAG pipelines, and text-to-SQL (98% accuracy); design evaluation sets, measure hallucination and answer quality, and turn model outputs into insights non-technical users trust
- Raise the bar for the team—mentor data scientists and ML engineers, set standards for validation and statistical rigor, review work, and translate complex findings into clear stories for customers and leadership

You're Built For This If...

- 6–10 years of hands-on data science—proven track record of models that shipped and moved business metrics, not just notebooks and slide decks
- Deep statistical and ML foundations—time series forecasting (ARIMA/ETS, Prophet, gradient boosting, deep learning forecasters), causal inference (DiD, synthetic control, uplift, causal graphs), anomaly detection, experimental design
- Simulation and scenario modeling—built discrete-event, agent-based, Monte Carlo, or system dynamics models or digital twins of business processes, grounded in real data; SimPy, Mesa, AnyLogic, or optimization libraries (OR-Tools, Pyomo) a solid advantage
- Strong technical toolkit—Python, SQL, Pandas, Scikit-learn, PyTorch/TensorFlow, statsmodels, DoWhy/EconML or similar; comfortable with large, messy, multi-source enterprise data
- Business translation—turn "why did margins fall in the West region?" into rigorous analysis, then explain it to a CFO in plain language
- Mentorship mindset—guided other data scientists through reviews, pairing, and technical direction

Big Plus:

- Hands-on experience with LLMs and agentic systems—prompt engineering, RAG, LLM evaluation, tool-using agents, or text-to-SQL

MLOps exposure—MLflow, Docker, AWS/GCP/Azure

Background in B2B SaaS, analytics products, or domains like retail, pharma, logistics, or finance

Why Fire AI (The Real Talk)

- Science That Drives Decisions: Your models power causal analysis, forecasting, anomaly detection, and simulations—enterprises rely on your work for million-dollar decisions daily
- Work at the Frontier:



Combine classical data science with multi-agentic systems, RAG, LLM orchestration, text-to-SQL at production scale serving 120+ category-defining brands
- Move at Warp Speed: $28.3K to $512.9K MRR in 7 months—your work fuels a $300M+ revenue trajectory and aggressive global expansion
- Problems You Won't Find Elsewhere: 17+ industries, 90+ languages, 700+ integrations—diverse data, hard modeling problems, real impact
- Get Rewarded: Competitive compensation + equity in a zero-churn startup backed by tier-1 VCs + clear path to Principal Data Scientist → AI Architect

What Winning Looks Like Month 3: Audited existing forecasting and anomaly models, shipped your first measurable improvement, set up evaluation benchmarks for core models

Month 6: Delivered a new causal or driver-analysis capability in production, shipped a first simulation or what-if scenario tool for a key customer use case, launched an experimentation framework for AI features, began mentoring 2+ team members

Month 12: Owned the science roadmap for forecasting, causal analytics, and simulation, pushed forecasting accuracy beyond 94%, established LLM insight-quality evaluation, became the go-to technical voice for data science

What We're Looking For (Honest Truth)

This isn't a reporting role—you'll build models that ship. This isn't pure research—your work has to hold up in production and make sense to a CFO. It's applied, high-ownership data science where your models power causal chains, forecasting, simulations, and AI insights enterprises depend on.

If you want to write analyses nobody acts on, this isn't it.

If you want to own the science behind the world's best decision intelligence platform, mentor a growing team, and work at the intersection of classical data science and agentic AI at a zero-churn hypergrowth startup going global, let's talk.

Career Path

Senior Data Scientist → Principal Data Scientist → AI Architect → Director of Data Science Core Values: Customer First | Execution Excellence | Act Like an Owner | Win Together

Vision: To build an agentic AI analytics universe that transforms reporting into diagnosis and insights into action.

Ready to build something real? Fire AI is an equal opportunity employer where your work speaks louder than your resume.

Empowering Business To build an agentic AI analytics universe that transforms reporting into diagnosis and insights into action.

www.fireai.in | [email protected]

📌 Senior Data Scientist (Hyderabad)
🏢 FireAI
📍 Hyderabad