Data Scientist in Bengaluru, Karnātaka at Accellor

Accellor is an AI-native services firm focused on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. We help global innovators and Fortune 100 enterprises modernize their architectures, scale operations, and operationalize intelligence across the enterprise.

Our core focus centers on transforming advanced AI from proofs-of-concept into production-grade systems. Spanning AI solutions, robust data services, enterprise applications, and product engineering — we tailor our offerings to the distinct operational needs of industries such as healthcare, life sciences, telecom, retail, financial services, and technology.

Operating as an extension of our clients' teams, we combine design thinking with technology-agnostic architectures to ensure faster time-to-value and seamless system interoperability. From initial strategy to full-scale deployment, we build intelligent, connected ecosystems that drive sustained enterprise value and redefine the future of transformation.

We are hiring a hands-on Data Scientist with 4–6 years of experience building and shipping classical machine-learning models on enterprise business data. The role sits in a central AI team at an enterprise client that serves the whole business, so the problems change with demand: sales, marketing, pricing, operations, procurement, HR, finance.

The core of the role is classical, hands-on data science: supervised learning - classification, regression, forecasting - on structured, tabular data, taken from business question to validated model to production handover. Generative-AI and LLM skills are welcome on top of that foundation, as the team applies both where each fit.

• Pick up problems from any function as the business needs them and frame them as supervised-learning tasks — e.g. deal win/loss and churn classification, price and margin prediction, demand and revenue forecasting, lead scoring and customer segmentation for marketing, supplier risk and spend classification in procurement, attrition prediction in HR, anomaly detection in operations.

• Build, tune and validate models end to end: feature engineering, model selection, cross-validation, hyperparameter tuning, calibration and error analysis.

• Work with structured enterprise data from CRM, ERP, product master and finance systems — profiling, cleaning, joining and resolving data-quality gaps before modelling.

• Run exploratory analysis and statistical tests (hypothesis testing, A/B analysis, causal reasoning) to back recommendations with evidence.

• Explain model behaviour to business stakeholders using interpretability techniques (SHAP, feature importance, partial dependence) and plain-language insight.

• Package models for production with engineering teams: reproducible pipelines, versioned artefacts, batch or API scoring, monitoring for drift and performance decay.

• Define and track success metrics (precision/recall, AUC, MAPE, lift, uplift) and the business KPIs they map to.

• Document approach, assumptions and results; present findings to leadership.

• 4–6 years as a data scientist or ML practitioner, with a few models delivered into production use.

• Strong grounding in classical ML: logistic regression, tree ensembles (random forest, XGBoost / LightGBM / CatBoost), linear and regularised regression, SVMs, clustering, and time-series forecasting (ARIMA, Prophet, gradient-boosted forecasting).

• Solid statistics: probability, distributions, hypothesis testing, confidence intervals, bias/variance, handling class imbalance and leakage.

• Expert Python for data science — pandas, NumPy, scikit-learn, statsmodels — and strong SQL on large relational or lakehouse datasets.

• Experience with at least one enterprise data platform: Databricks, Microsoft Fabric, Snowflake or similar, including Spark / PySpark for large data.

• Feature engineering on messy, real-world business data; comfortable with missing values, categorical encoding, time-based features and entity resolution.

• MLOps basics: experiment tracking (MLflow), model versioning, pipeline scheduling, and monitoring in production.

• Ability to translate a business question into a modelling plan and the result back into a decision — strong stakeholder and communication skills.

• Degree in a quantitative field (statistics, mathematics, computer science, engineering, economics or similar).

• Experience with deep learning and neural networks (PyTorch or TensorFlow / Keras) — MLPs on tabular data, sequence models for time series, or embeddings — and judgement on when they beat classical methods.

• Working knowledge of LLMs, generative AI or agent frameworks, applied alongside classical models — a strong plus. Hands-on with one or more of Microsoft Agent Framework (MAF), LangChain, LangGraph, CrewAI or Semantic Kernel.

• Experience modelling sales operations, pricing, quoting or supply-chain data.

• Pricing, margin or revenue modelling: price elasticity, win-probability curves, discount optimisation, deal-impact analysis.

• Exposure to one business function such as HR, operations or procurement, with examples of models that changed a decision there.

• Experience with Salesforce data (opportunities, quotes, accounts) as a modelling source.

• Familiarity with causal inference or uplift modelling.

• Azure data and ML stack (Azure ML, Fabric, Databricks on Azure)

Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centres.

Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.

Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings.

Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Personal Accident Insurance, Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.

Accellor is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic