SaveIN is hiring for a fresher entry level Data Scientist role in India

Position:

• Data Scientist

• Entry-level opening within the Data Science & Risk team

• Core focus on risk analytics, predictive modeling, and large-scale dataset management

Company:

• SaveIN, a Y-Combinator-backed healthcare technology company

• Fintech platform bridging healthcare access and embedded finance solutions

• Investor-supported by global institutions from the US and Europe

Location:

• Gurugram, Haryana, India

Job type:

• Full-time position

Job mode:

• On-site role at company headquarters in Gurugram

Job requisition id:

• Not explicitly provided in the JD

Years of experience:

• 0 to 2 years relevant industry experience preferred

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Company Description

• SaveIN is an emerging player in the Indian healthcare-fintech intersection.

• The company has been backed by Y-Combinator, one of the world’s most influential startup accelerators, indicating trust, scalability, and global recognition.

• With headquarters in Gurugram, SaveIN is focused on transforming how healthcare services are accessed and financed in India.

• The company leverages financial technology to improve affordability of treatments, diagnostics, and wellness services by embedding finance directly into healthcare providers’ offerings.

• Their products span four key verticals: healthcare aggregation, embedded payments, consumer finance, and SaaS platforms tailored for healthcare providers.

• They serve patients, doctors, and clinics with solutions designed to make healthcare more accessible and efficient.

• The backing by US and European investors ensures robust capital and mentorship, positioning SaveIN as a leading innovator in this space.

• In just a short span, SaveIN has shown measurable growth, doubling its engineering workforce in two years and maintaining steady expansion across functions.

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Profile Overview

• The role of Data Scientist at SaveIN is positioned within the Data Science & Risk function.

• Candidates joining this position will be entrusted with working on real-world financial data, particularly credit bureau datasets and alternate data signals.

• Core responsibilities revolve around the design and deployment of machine learning models that directly impact the firm’s underwriting systems, customer portfolio management, and risk assessment processes.

• This role allows early-career professionals to gain exposure to fintech-specific data applications, from predictive modeling to automated scoring pipelines.

• The position demands strong programming ability, structured thinking, and a clear understanding of how models can be translated into scalable business solutions.

• The Data Scientist here is expected to partner with stakeholders across multiple departments including Risk, Product, and Operations.

• They will also contribute to data governance and documentation practices, ensuring reproducibility and transparency of every analytical workflow.

• This role provides an accelerated career track for individuals keen to apply machine learning to finance and healthcare contexts.

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Qualifications

• Educational background should include a Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related analytical discipline.

• Entry-level candidates with up to 2 years of work experience in analytics, machine learning, or applied data science are eligible.

• Proficiency in Python (libraries such as pandas, numpy, scikit-learn) and SQL is essential.

• Candidates should possess familiarity with classification, regression, and ensemble modeling techniques.

• A basic understanding of credit bureau datasets (CIBIL, CRIF, Experian) and alternate datasets is desirable, though not mandatory.

• Knowledge of risk modeling concepts, including scorecards and credit decision systems, will be advantageous.

• Experience with visualization tools such as PowerBI or Tableau is appreciated.

• Exposure to Git-based version control, cloud computing platforms like AWS/GCP/Azure, and distributed systems such as Spark, Hadoop, or BigQuery is seen as a plus.

• Candidates should demonstrate strong logical reasoning, communication, and problem-solving abilities to excel in SaveIN’s fast-paced fintech environment.

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Additional Info

• The role will involve the end-to-end lifecycle of production-grade model building, right from data extraction and feature engineering to validation and deployment.

• The Data Scientist will gain first-hand experience in building risk models that impact customer acquisition, collections strategies, and credit monitoring.

• SaveIN emphasizes a collaborative work environment where innovation, experimentation, and data-driven decisions are encouraged.

• Professionals in this role will have opportunities to work alongside senior scientists and contribute to high-stakes projects that impact both business performance and customer trust.

• The company offers competitive pay packages, benefits, and a strong emphasis on professional growth.

• With healthcare affordability being a pressing issue in India, employees at SaveIN directly contribute to improving the nation’s healthcare financing landscape.

• The role is particularly suited for individuals who are proactive, detail-oriented, and motivated by impact-driven work.

• SaveIN is looking for team players who can adapt quickly, manage ambiguity, and provide clarity in data-backed decision-making processes.

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Key Responsibilities (Expanded)

• Design and deploy machine learning models for underwriting, customer risk evaluation, and credit monitoring.

• Engineer robust features from bureau data, transactional activity, and other alternate datasets to improve predictive accuracy.

• Build and manage reporting systems such as PowerBI dashboards, ensuring stakeholders have visibility into risk and portfolio health.

• Automate recurring workflows to reduce operational bottlenecks, ensuring systems are scalable and efficient.

• Conduct exploratory analysis, identify behavioral patterns, and translate findings into actionable recommendations.

• Work closely with internal teams across Risk, Product, and Operations to design solutions aligned with business priorities.

• Document all models, experiments, and workflows for transparency, replication, and long-term usability.

• Monitor data pipelines for integrity, validate data quality, and manage processes for cleansing and error detection.

Please click here to apply.