Cloudsufi - Senior Quantitative Data Scientist - Python
CloudSufi
About CloudSufi:CloudSufi is a global AI and data engineering company that helps enterprises turn complex data into smart, confident decisions. We work across industries like CPG, retail, energy, and financial services, combining AI, cloud, and analytics to solve real business problems at scale.About the Role:We're looking for a Senior Quantitative Data Scientist - research & modelling, who can build reliable, well-thought-out models to answer tough business, market, and economic questions. You'll take messy, real-world data and turn it into clear, defensible forecasts and insights that leaders can actually act on.What You'll Do:- Build forecasting and scenario models for markets, technology trends, demand, and business economics- Collect, clean, and validate data from multiple sources - reports, APIs, filings, databases- Run exploratory analysis to spot trends, gaps, and anomalies in data- Test and refine models by comparing forecasts to real outcomes- Document assumptions and methods so models are transparent and easy to reuse- Use AI/LLM tools to speed up research, coding, and analysis - and validate their outputs- Present findings clearly to both technical and non-technical stakeholders- Collaborate with engineers and analysts to move models into production- Guide and mentor junior team membersWhat You Need:- 58+ years in data science, quantitative research, forecasting, or econometrics- Strong Python skills (pandas, NumPy, SciPy, scikit-learn) and solid SQL- Good grounding in statistics, time-series analysis, and model validation- Comfortable working with incomplete or messy real-world data- Experience building models from scratch when standard approaches don't fit- Strong communication skills - can explain complex analysis simplyGood to Have:- Background in hedge funds, investment research, or financial markets analysis- Experience with market/tech disruption, demand, or investment modelling- Familiarity with Bayesian methods, Monte Carlo, or causal inference- Exposure to cloud platforms, data pipelines, or production model deployment- Hands-on experience using LLMs/agentic tools in research work (ref:hirist.tech)
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