Data Science Engineer

":" About the Role

Rubiscapes Data Science Engineer sits at the productive boundary between data science rigour and software engineering discipline translating complex analytical findings into robust, repeatable, and scalable artefacts embedded in the platform. You will partner with domain experts across BFSI, manufacturing, and healthcare to solve high-value decision problems using statistical modelling, machine learning, and causal inference, then ensure those solutions graduate from notebook to production within our 90-day deployment promise. This role contributes directly to Rubiscapes track record of delivering 3 faster pipelines and 40% reduction in decision latency for enterprise customers.

Key Responsibilities

Frame ambiguous business problems as precise analytical questions, define success metrics, and design experiments that produce statistically defensible results.

Build end-to-end data science solutions from exploratory data analysis and feature engineering through model selection, validation, and production packaging using Python, pandas, and scikit-learn.

Develop domain-adapted models for Rubiscapes core industry verticals: credit risk and fraud detection (BFSI), predictive maintenance (manufacturing), and patient outcome modelling (healthcare).

Collaborate with data engineers on RubiFlow to translate ad-hoc analytical pipelines into governed, scheduled, and monitored production pipelines.

Create explainability artefacts (SHAP, LIME, integrated gradients) for models deployed in regulated environments, and document model cards for the RubiStudio registry.

Drive structured A/B and champion-challenger experiments to validate model improvements before full rollout, integrating with RubiSight for result visualisation.

Mentor junior analysts and contribute to Rubiscapes Industry-Academia COE programme by translating research papers into practical platform capabilities.

Requirements Requirements

3+ years in a data science or analytical engineering role delivering models to production in enterprise environments.

Expert-level Python for data analysis: pandas, NumPy, scipy, statsmodels, and scikit-learn; confident with SQL across large analytical datasets.

Strong grounding in statistical inference, experimental design, and the ability to distinguish signal from noise in messy enterprise data.

Experience with at least one domain-specific modelling area: fraud/risk scoring, demand forecasting, churn prediction, anomaly detection, or survival analysis.

Familiarity with ML experiment tracking (MLflow or equivalent) and a structured approach to documenting model assumptions and limitations.

Bachelors or Masters degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field.

Nice to Have

Experience applying causal inference methods (DiD, IV, propensity score matching) to evaluate business interventions in enterprise settings.

Exposure to time-series forecasting at enterprise scale using Prophet, NeuralProphet, or deep learning architectures (N-BEATS, TFT).

Familiarity with Bayesian modelling frameworks (PyMC, Stan) for uncertainty quantification in regulated decision contexts.

Published case studies or conference presentations on applied data science in BFSI, manufacturing, or healthcare.

About Rubiscape

Rubiscape is Indias leading Decision Intelligence Platform, unifying data engineering, BI, machine learning, and agentic AI in a single governed platform. Built in Pune and trusted by Fortune 500 enterprises across BFSI, manufacturing, healthcare, and government. 8 international innovation patents. 10 Industry-Academia Labs COEs. From BI to AI One Platform. Every Decision.

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