Data Scientist

Job Title

Location

Business Unit:

Why this role exists

Fusion Business Solutions (P) Ltd. helps clients turn operational data into competitive advantage. As a Data Scientist you will design and deliver predictive models, machine-learning pipelines and data-driven insights that accelerate decision-making for both internal stakeholders and external customers.

What you will do:

• Translate high-level business goals into data questions and measurable success criteria.
• Acquire, clean and combine large structured and unstructured datasets; document assumptions and data-quality checks.
• Explore data to uncover patterns, drivers and anomalies; present findings through clear visual stories and executive briefs.
• Build, validate and tune forecasting, classification, regression, recommendation and clustering models with modern open-source libraries.
• Package models for production with reproducible code, versioned artefacts and automated tests; deploy via REST APIs, batch jobs or embedded BI dashboards.
• Monitor accuracy, drift and usage; schedule retraining or rollback when thresholds are breached.
• Collaborate with data engineers on pipeline design and with BI developers on dashboard integration.
• Advise on A/B-test design and causal-impact analysis.
• Coach analysts and junior team members in statistical thinking, coding best practices and responsible AI principles.

Technology you are likely to touch

• Python (pandas, scikit-learn, PyTorch/TensorFlow)
• SQL
• Spark or PySpark
• Docker, MLflow (or similar)
• Airflow/DBT
• GitHub Actions
• Power BI (or equivalent)
• At least one major cloud environment (AWS, GCP or Azure)
• Familiarity with generative-AI frameworks, NLP libraries or computer-vision toolkits is a plus.

Must-have qualifications

• Three or more years of hands-on experience building and deploying predictive-analytics or machine-learning solutions that delivered measurable business value.
• Strong SQL skills and experience modelling datasets larger than 100 GB.
• Proficiency in Python (or R) for data analysis, modelling and automation.
• Practical understanding of model lifecycle management—including experiment tracking, version control, containerisation and CI/CD.
• Ability to explain technical concepts to non-technical audiences and to write concise technical documentation.

Good-to-have extras

• Insurance domain exposure

  —experience working with policy, claims, premium or commission data; familiarity with underwriting or fraud-detection use cases; understanding of regulatory frameworks such as HIPAA, NAIC or IFRS 17.
• Experience in other FBSPL focus areas—accounting/BFSI workflows (general ledger, AP/AR), e-commerce fulfilment, RPA-driven back-office processes, or data-annotation quality control.
• Cloud-native ML services (AWS SageMaker, Google Vertex AI, Azure ML) and MLOps best practices.
• Time-series forecasting, optimisation algorithms or deep-learning frameworks.
• Certifications such as AWS Machine Learning Specialty, Microsoft DP-100, UiPath RPA Developer, or equivalent.