Data Scientist

I2interfaces; Bengaluru; On-site; INR 1,440,000 - 1,760,000; Full time

Job summary

I2interfaces is seeking an experienced Data Scientist in Bengaluru to build end-to-end machine learning solutions that drive measurable business impact. The candidate should have strong foundations in machine learning and statistical modeling, collaborating closely with business and engineering teams.

Responsibilities include model development, performance monitoring, and ensuring scalable deployment in AWS environments. A Bachelor's or Master's degree in a quantitative field and 3+ years of experience are mandatory. Candidate should have advanced proficiency in Python and SQL.

Qualifications

• 3+ years of hands-on experience as a Data Scientist.
• Proven record of building and deploying ML models.
• Experience in Agile/Scrum environments.

Responsibilities

• Own the full ML lifecycle from data collection to deployment.
• Build and deliver statistical or ML models independently.
• Collaborate with business stakeholders to understand requirements.

Skills

Machine Learning

Statistics

Statistical Modeling

Python

SQL

Education

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field

Tools

AWS

Pandas

NumPy

Scikit-learn

Job description

# Data ScientistBangalore, India ₹1600000.00 PA 2 months agoFull-time3.0+ yrs experienceRequired SkillsAWSSQLScikit-learnPandasNumPyMachine LearningStatisticsPythonStatistical ModelingAbout the Role## Data Scientist - ABOUT THE ROLEWe are looking for an experienced Data Scientist with a strong foundation in traditional machine learning and statistical modeling to join our growing analytics team. The ideal candidate is someone who thrives in building end-to-end ML solutions — from raw data to production-deployed models — and can collaborate effectively with business and engineering teams to deliver measurable impact.This is a hands-on individual contributor role. We are specifically looking for traditional Data Scientists with strong ML and statistics fundamentals — not Data Engineers or BI professionals.### BUSINESS OBJECTIVES* Maintain, monitor, and continuously enhance existing machine learning models in production. * Identify and build new ML use cases that drive measurable business impact. * Apply advanced statistical and machine learning techniques to solve real-world business problems. * Partner with business stakeholders to translate complex problems into data-driven solutions. ### KEY RESPONSIBILITIES* Own the full ML lifecycle: data collection, data preparation, feature engineering, model development, validation, and deployment. * Build and deliver at least 1–2 statistical or machine learning models independently from scratch. * Collaborate with business stakeholders to understand requirements and translate them into scalable AI/ML solutions. * Ensure post-deployment model performance monitoring, continuous improvement, and retraining as needed. * Work closely with data engineering teams to ensure scalable model deployment in AWS cloud environments. * Communicate findings, model performance, and recommendations clearly to both technical and non-technical audiences. * Stay up to date with traditional statistical approaches as well as modern AI/ML methodologies. ### TECHNICAL SKILLS REQUIRED#### Must Have* **Machine Learning:** Hands-on experience with supervised and unsupervised algorithms (Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, Clustering, etc.) * **Statistics & Mathematics:** Strong understanding of probability, hypothesis testing, statistical inference, distributions, and applied mathematics. * **Statistical Modeling:** Demonstrated ability to build, evaluate, and interpret statistical models for real business use cases. * **Python:** Advanced proficiency — data manipulation, model building, evaluation, and pipeline development using Pandas, NumPy, Scikit-learn, etc. * **SQL:** Strong SQL skills for data extraction, transformation, and feature creation (joins, CTEs, window functions, aggregations). * **Practical ML Deployment:** Must have independently built and deployed at least 1–2 production-grade ML models. #### Good to Have* **Cloud Exposure:** Familiarity with AWS (S3, SageMaker, EC2, Glue) or any cloud ecosystem for ML model deployment. * **MLOps Basics:** Experience with model monitoring, experiment tracking (MLflow), CI/CD pipelines, and containerization (Docker). * **Domain Knowledge:** Prior experience in Insurance or Banking is preferred. However, supply chain, e-commerce, FMCG, auto, or retail is also acceptable. * **Visualization:** Ability to present insights using Power BI, Tableau, or Python-based libraries (Matplotlib, Seaborn). * **Generative AI:** Exposure to GenAI/LLMs is a bonus — not a mandatory requirement for this role. ### WHAT WE ARE NOT LOOKING FOR* ✗ Data Engineers Profiles focused primarily on building ETL/ELT pipelines, data warehousing, or infrastructure — without evidence of building ML models. * ✗ BI / Reporting Analysts Profiles primarily focused on dashboards, Power BI, Tableau, or data storytelling without hands-on model building. * ✗ GenAI-Only Specialists Profiles where the primary experience is limited to LLMs, RAG pipelines, prompt engineering, or chatbot development without foundational ML and statistics. ### PREFERRED QUALIFICATIONS* 3+ years of hands-on experience as a Data Scientist (not Data Engineer, BI Analyst, or BA). * Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field. * Strong analytical and problem-solving mindset with the ability to break down complex business problems. * Proven record of building and deploying at least 1–2 statistical or ML models that delivered business value. * Experience working in Agile/Scrum environments and collaborating across cross-functional teams. * Prior exposure to Insurance, Banking, Supply Chain, E-Commerce, FMCG, or Automotive domains.