Senior Data Scientist II

InXiteOut; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

InXiteOut is seeking a Senior Data Scientist with 6–8+ years of experience to lead enterprise ML initiatives in Bengaluru. You will design forecasting systems, develop production-grade data quality frameworks, and mentor junior data scientists, leveraging time-series and NLP techniques, MLOps, and cloud pipelines (AWS/Azure).

The role requires advanced Python/PySpark/SQL skills, Databricks experience, and a track record of deploying scalable ML models in enterprise settings.

Qualifications

• 6–8+ years of experience building and deploying scalable ML models in enterprise environments.
• Strong programming skills in Python, PySpark, SQL with Databricks experience.
• Expertise in time-series modelling and NLP techniques.
• MLOps, DevOps, Docker, Git/GitHub, and cloud deployment pipelines (AWS, Azure).

Responsibilities

• Lead design and deployment of forecasting systems with time-series modelling and monitoring.
• Develop robust data quality frameworks for production pipelines.
• Create forecasting systems with 24-month horizons using historical data and features.
• Design ensemble models to isolate demand drivers.
• Build reusable training and deployment pipelines for consistency.
• Implement performance monitoring to detect drift and issues.
• Integrate and monitor LLM-based systems in Azure AI / AWS Bedrock.
• Drive AI governance with monitoring and safety controls.

Skills

Python

PySpark

SQL

Databricks

Random Forest

Scikit-learn

K-Means

KNN

Linear Regression

Naive Bayes

Time Series

NLP

NLP tools

MLOps

DevOps

Docker

GitHub

Git

AWS

Azure

Tools

Databricks

Spark

Hadoop

MLflow

Kubeflow

Weights & Biases

Job description

Job Description: Senior Data Scientist (6-8+ Years Experience)

Job Summary

We are seeking a highly experienced and innovative Senior Data Scientist with over 6+ years of expertise in core data science concepts and around 2 years of focused, hands-on experience in Machine Learning model development. You will lead strategic AI/ML initiatives, mentor junior data scientists, and deliver intelligent solutions that drive business value using both classical and modern machine learning techniques.

Key Responsibilities

• Lead the design and deployment of enterprise-scale forecasting systems with a focus on time-series modelling, performance monitoring, and long-running production systems.
• Develop robust temporal data quality frameworks to handle missing data, irregular timing, and outliers directly within production pipelines.
• Create and maintain forecasting systems for demand prediction, generating long-term (e.g., 24-month) forecasts using historical patterns, external factors, and advanced feature engineering.
• Design interpretable ensemble approaches, combining multiple regression models with trend and seasonal decomposition to isolate key demand drivers.
• Engineer reusable model training and deployment pipelines to improve consistency and reduce setup time across data science teams.
• Implement rigorous performance monitoring to detect forecast issues, analyze drift, identify unusual shifts in data patterns, and prevent downstream model degradation.
• Spearhead the integration and monitoring of LLM-based systems, including stability analysis and cost-forecasting modules within Azure AI and AWS Bedrock workflows.
• Drive AI governance by establishing model monitoring, safety frameworks, and performance controls for secure enterprise adoption.

Required Skills

• Experience: 6-8+ years of proven expertise in building and deploying scalable machine learning models in enterprise environments.
• Programming & Big Data: Advanced proficiency in Python, PySpark, SQL. Strong hands-on experience with Databricks is mandatory.
• Machine Learning Core: Random Forest, Scikit-learn, K-Means/KNN, Linear Regression, and Naive Bayes.
• Time Series & NLP: Strong background in temporal data exploration, pattern recognition, anomaly detection, and NLP tools (NLTK, Spacy).
• MLOps & Deployment: Expertise in MLOps, LLM Ops, DevOps, Docker, Git/GitHub, and cloud deployment pipelines (AWS, Azure).

Optional/Nice-to-have Skills

• MLOps: Model tracking, monitoring, CI/CD with MLflow, Kubeflow, etc.
• Big Data Tools: Spark, Databricks, or Hadoop ecosystem familiarity
• Experiment Tracking: Tools like Weights & Biases, MLflow

Certifications (Preferred But Not Mandatory)

• Google Cloud or Azure AI Engineer / Data Scientist Associate
• Databricks Certified Machine Learning Professional