Senior Software Engineer - Data Science

About Us

CLOUDSUFI, a Google Cloud Premier Partner, is a global leading provider of data-driven digital transformation across cloud-based enterprises. With a global presence and focus on Software & Platforms, Life sciences and Healthcare, Retail, CPG, financial services and supply chain, CLOUDSUFI is positioned to meet customers where they are in their data monetization journey.

Our Values

We are a passionate and empathetic team that prioritizes human values. Our purpose is to elevate the quality of lives for our family, customers, partners and the community.

Equal Opportunity Statement

CLOUDSUFI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified candidates receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation and national origin status. We provide equal opportunities in employment, advancement, and all other areas of our workplace. Please explore more at https://www.cloudsufi.com/

• Role Overview
  
  

CLOUDSUFI is seeking a Senior Data Scientist to design, build, validate, and continuously improve quantitative models for technology, market, economic, and business analysis.

The role requires strong first-principles modelling, forecasting, data analysis, and research skills. The successful candidate will convert complex analytical questions into transparent, reproducible models that combine historical data, domain assumptions, scenario analysis, and expert judgement.

This is a hands-on role involving data collection, exploratory analysis, model development, validation, documentation, and communication of results to technical and business stakeholders.

• Key Responsibilities
  
  

Quantitative Model Development

• Translate complex business and research questions into clearly defined variables, assumptions, equations, and model outputs.
• Develop models for:
• Technology cost and capability trajectories
• Adoption and market-share forecasting
• Product and commodity demand
• Supply, capacity, utilisation, and bottlenecks
• Productivity, labour, and economic impacts
• Company economics and investment implications
• Build transparent models that clearly separate historical data, assumptions, calculations, scenarios, and outputs.
• Select modelling approaches appropriate to the problem rather than applying complex techniques without clear analytical value.
  
  

Forecasting and Scenario Analysis

• Develop time-series, causal, econometric, statistical, and machine-learning forecasting models.
• Model nonlinear change, tipping points, S-curves, substitution, market saturation, and incumbent decline.
• Build base, upside, downside, and alternative methodological scenarios.
• Conduct sensitivity analysis to identify the assumptions and variables that most affect results.
• Reconcile top-down forecasts with bottom-up operational, product, capacity, or task-level models.
  
  

Data Research and Analysis

• Identify the data required to answer analytical questions and evaluate its suitability, coverage, quality, and limitations.
• Collect and integrate structured and unstructured data from databases, APIs, filings, research reports, public sources, and external data providers.
• Clean, standardise, reconcile, and validate data across units, currencies, geographies, entities, and time periods.
• Perform exploratory analysis to identify trends, structural breaks, correlations, anomalies, and missing information.
• Define defensible proxies and estimation methods where direct data is unavailable.
  
  

Model Validation and Continuous Refinement

• Backtest models and compare forecasts with actual observed outcomes.
• Diagnose forecast errors and distinguish between data issues, assumption errors, methodological limitations, and genuine market changes.
• Establish validation checks for mathematical correctness, internal consistency, boundary conditions, and economic plausibility.
• Track model assumptions, parameters, datasets, versions, and changes over time.
• Continuously refine methodologies as new data, evidence, and expert feedback become available.
  
  

Analytical Standards and Reproducibility

• Develop modular, reusable modelling components rather than isolated analytical scripts.
• Create reproducible pipelines for data preparation, modelling, validation, scenario generation, and reporting.
• Maintain clear documentation covering methodology, assumptions, limitations, and interpretation.
• Implement automated quality checks and testing for analytical code and model outputs.
• Support common data contracts, model interfaces, metadata standards, and output structures across analytical workstreams.
  
  

AI-Supported Modelling and Research

• Use LLMs and AI-supported workflows for research, data extraction, hypothesis generation, code development, and analytical review.
• Validate AI-generated findings, calculations, assumptions, and code before incorporating them into models.
• Work with AI engineering teams to operationalise analytical methods as reusable workflows, tools, or agents.
• Help define evaluation criteria for assessing the factual, mathematical, and methodological quality of AI-generated analysis.
  
  

Communication and Collaboration

• Explain model logic, assumptions, uncertainties, and results clearly to technical and non-technical stakeholders.
• Create decision-ready charts, tables, analytical summaries, and model documentation.
• Work closely with domain experts to incorporate functional knowledge and resolve methodological gaps.
• Collaborate with data engineers, AI engineers, software engineers, analysts, and product teams to move models into production.
• Review analytical work and mentor junior data scientists and analysts.
• Required Skills and Experience
• 5–8+ years of experience in data science, quantitative research, forecasting, econometrics, operations research, or applied modelling.
• Strong experience building forecasting, simulation, scenario, or decision-support models.
• Advanced proficiency in Python and its analytical ecosystem, including pandas, NumPy, SciPy, scikit-learn, and statistical modelling libraries.
• Strong understanding of time-series analysis, regression, probability, statistical inference, optimisation, and model validation.
• Ability to build models from first principles where established datasets or methodologies are incomplete.
• Experience working with noisy, sparse, inconsistent, or partially observed real-world data.
• Strong SQL skills and experience working with relational, analytical, and API-based data sources.
• Experience building reproducible analytical pipelines using version control, testing, and structured documentation.
• Ability to communicate complex quantitative findings clearly and defend modelling choices.
• Strong attention to numerical accuracy, assumption consistency, and analytical traceability.
• Good to Have
• Experience modelling technology adoption, market disruption, cost curves, supply and demand, commodities, labour markets, or investment outcomes.
• Familiarity with logistic, Bass diffusion, survival, cohort, stock-and-flow, system-dynamics, or Monte Carlo models.
• Experience with causal inference, panel data, Bayesian methods, or probabilistic forecasting.
• Experience with financial statements, company analysis, value-chain analysis, or investment research.
• Experience using graph data, knowledge graphs, or entity-relationship models for analytical applications.
• Familiarity with cloud platforms, workflow orchestration, data pipelines, model registries, and production model deployment.
• Experience using commercial market and financial datasets such as FactSet, Bloomberg, Haver, or similar platforms.
• Practical experience using LLMs and agentic systems in research and quantitative workflows.
• What We Are Looking For
  
  

We are looking for a rigorous and pragmatic model builder who can move from an ambiguous question to a defensible analytical framework and working quantitative model.

The right candidate should be comfortable challenging assumptions, working with imperfect data, making uncertainty explicit, and choosing simple, explainable methods where they are more appropriate than unnecessary complexity.

They should combine strong quantitative foundations with business judgement, intellectual curiosity, attention to detail, and the ability to turn analytical work into repeatable production capabilities.