Data Scientist

Infiniti Software Solutions Pvt Ltd.; Chennai District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Infiniti Software Solutions Pvt Ltd. is seeking a Data Scientist in Chennai with a strong product mindset. You will architect machine learning models, prototypes, and data strategies to power product intelligence and decision-making.

You will own the analytics engine today and help shape the product roadmap, balancing model complexity with business impact while collaborating with engineering and product teams.

Qualifications

• 4+ years of experience as Data Scientist.
• Strong Python and SQL skills.
• Experience building ML models and pipelines.
• Experience producing production-grade data science solutions.

Responsibilities

• Architect data-driven models to power product features.
• Prototype using Python and validate mathematical logic.
• Define data requirements and tracking events for improved signals.
• Influence product roadmap through data-driven narratives.

Skills

Python

SQL

Pandas

NumPy

Scikit-Learn

Data Visualization

Feature Engineering

Tools

Matplotlib

Seaborn

SciPy

Jupyter

Job description

Job Title: Data Scientist
Location: Chennai
Work Mode: Work From Office
Experience: 4+ Years
Employment Type: Full-Time

Job Summary

We are seeking a Data Scientist with Product Mindset who treats code as a means to a strategic end. You will be the architect of our product’s intelligence, building the mathematical logic and predictive models that drive our product path.

This role is designed for a builder who wants to evolve into a leader. You will own the analytical engine of the company today, with a defined pathway to owning the Product Roadmap tomorrow.

Your Core Mission

1. Architect the Logic (Modeling & Prototyping)

Build the "Gold Standard":

You will design, train, and validate the core machine learning models (Classification, Regression, Forecasting) that solve our most critical business problems.

Rapid Prototyping:

You will use Python to build functional proofs-of-concept. You deliver the validated logic and mathematical framework that our Engineering team will scale.

Algorithmic Strategy:

You decide how we solve a problem. Is this a rules-based engine? A simple regression? Or a complex deep learning solution? You own the trade-off between complexity and business value.

2. Define the Data Strategy

Signal Discovery:

You will not just use the data we have; you will dictate the data we need. You will design the feature requirements and tracking events necessary to capture improved user signals.

Metric Definition:

You will move beyond vanity metrics. You will define the "North Star" KPIs (e.g., Retention, CLV, Engagement Quality) that align our algorithms with actual business health.

Hypothesis Generation:

You will mine our data landscape to find user friction points and propose data-backed features to fix them.

Statistical Rigor:

You will design the framework for A/B tests and product experiments, ensuring we distinguish between random noise and actionable trends.

Roadmap Influence:

You will present data narratives to leadership that directly influence what we build next.

The Ideal Candidate Profile

The Technical Toolkit:

Proficient in Python & SQL (ML Native):

You are fluent in the Data Science stack (Pandas, NumPy, Scikit-Learn) and complex SQL. You write clean code to explore data and build models, not to develop applications.

Strong Statistical Foundation:

You have a deep grasp of probability, hypothesis testing, and error analysis. You understand why a model works, not just how to import it.

Data Visualization:

You can build compelling visualizations (Matplotlib, Seaborn) that make complex data obvious to non-technical stakeholders.

The Product Mindset:

Outcome Over Output:

You care more about moving a business metric than optimizing a hyperparameter. You understand that a simple model that solves a user problem is infinitely better than a complex one that never ships. You optimize for business KPIs (ROI, Retention), not just model performance metrics (AUC/F1).

User Empathy:

You constantly ask, "How does this model improve the user's life?"

Communication:

You don't just translate the trade-offs you align. You should be able to explain the "Business Value" of an algorithm to the Executives and the "Mathematical Logic" to an Engineer.

Years of Experience - 4+ years as Data Scientist