Data Scientist

InfraCents Pvt. Ltd.; India; Remote; INR 900,000 - 1,500,000; Full time

Job summary

InfraCents Pvt. Ltd. is seeking a data scientist to join client projects, frame business problems, analyze data, and build predictive models. You will forecast churn and propensity, segment data, detect anomalies and analyze experiments.

The role requires solid statistics, strong SQL and Python, and the judgment to prefer simple analyses when appropriate. You will communicate findings to both technical and business audiences.

Qualifications

• 1–3 years in data science or analytics with models or analyses that informed real decisions.
• Strong grounding in probability, hypothesis testing and model evaluation.
• Writes efficient SQL and clean, reproducible Python.
• Starts from the decision to be made, not the algorithm.
• Tells a clear story with data, including its limitations.

Responsibilities

• Frame business questions as measurable analytical or modelling problems.
• Build, validate and tune models for classification, regression, forecasting and clustering.
• Run exploratory analysis to find patterns, data quality issues and opportunities.
• Design and analyse A/B tests and other experiments with sound statistics.
• Estimate impact using appropriate methods and communicate uncertainty honestly.
• Work with data and ML engineers to deploy models and monitor their performance.
• Build dashboards and reports that stakeholders actually use.
• Present findings and recommendations clearly to technical and business audiences.

Skills

SQL

Python

Statistics

Communication

Business sense

Education

Bachelor's or Master's degree in related field

Tools

Jupyter

Git

Airflow

Job description

About InfraCents

InfraCents is a software engineering company. We design and build software products, run the cloud platforms they live on, build AI systems and data pipelines, and keep all of it secure, reliable and cost-efficient in production. Our engineers work directly with clients, own outcomes end to end and are trusted with real production systems. We value clear thinking, honest communication and work that still holds up a year after it ships.

Role Overview

The Data Scientist will work with client teams to frame business problems, analyse data, build predictive models and communicate findings that lead to decisions. Typical work includes forecasting, churn and propensity models, segmentation, anomaly detection and experiment analysis.

We are looking for someone with solid statistics, strong SQL and Python, and the judgement to know when a simple analysis beats a complex model.

Key Responsibilities: Analysis & Modelling

• Frame business questions as measurable analytical or modelling problems.
• Build, validate and tune models for classification, regression, forecasting and clustering.
• Run exploratory analysis to find patterns, data quality issues and opportunities.

Key Responsibilities: Experimentation

• Design and analyse A/B tests and other experiments with sound statistics.
• Estimate impact using appropriate methods and communicate uncertainty honestly.

Key Responsibilities: Production & Collaboration

• Work with data and ML engineers to deploy models and monitor their performance.
• Build dashboards and reports that stakeholders actually use.
• Present findings and recommendations clearly to technical and business audiences.

Who We're Looking For

• Experience: 1-3 years in data science or analytics with models or analyses that informed real decisions.
• Statistics: Strong grounding in probability, hypothesis testing and model evaluation.
• Data skills: Writes efficient SQL and clean, reproducible Python.
• Business sense: Starts from the decision to be made, not the algorithm.
• Communication: Tells a clear story with data, including its limitations.

Technical Expertise Required

• Languages: Python, SQL
• Analysis & ML: pandas / Polars, scikit-learn, XGBoost / LightGBM, statsmodels, PyTorch
• Data Platforms: Snowflake, BigQuery, Databricks, PostgreSQL, dbt
• Experiment Tracking: MLflow, Weights & Biases
• Visualisation: Power BI, Tableau / Looker, Streamlit, matplotlib / Plotly
• Tools: Jupyter, Git, Airflow (basic)

Nice to Have

• Experience with time-series forecasting or causal inference.
• Experience applying LLMs to analytics, such as text classification or natural-language querying.
• Experience in a specific domain such as e-commerce, fintech or SaaS.

Qualifications

• Experience: 1-3 years in data science, analytics or applied machine learning.
• Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, Engineering or a related field.