Senior Data Scientist II

Inxite Out; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Inxite Out is seeking a Senior Data Scientist with 6-8+ years of experience to lead forecasting and enterprise ML initiatives in Bengaluru. You will mentor analysts, develop time-series models, and oversee ML pipelines from development to production.

The role emphasizes scalable production systems, data quality, model governance, and integration of LLM-based capabilities in cloud platforms.

Qualifications

• 6-8+ years of proven expertise in building and deploying scalable machine learning models in enterprise environments.
• Proficiency in Python, PySpark, SQL; strong Databricks experience mandatory.
• Core ML methods including Random Forest, Scikit-learn, Linear Regression, K-Means/Naive Bayes.
• Time-series analysis, NLP basics, anomaly detection and pattern recognition.
• MLOps, deployment pipelines, Docker, Git/GitHub; cloud deployment (AWS/Azure).

Responsibilities

• Lead design and deployment of enterprise-scale forecasting systems with a focus on time-series modelling and production readiness.
• Develop robust data-quality frameworks for production pipelines handling missing data and outliers.
• Create forecasting systems for 24-month demand forecasts using historical patterns and external factors.
• Design interpretable ensemble models to isolate key demand drivers.
• Build reusable model training and deployment pipelines for consistency across teams.
• Implement performance monitoring to detect drift and data shifts in forecasts.
• Integrate and monitor LLM-based systems within Azure AI and AWS Bedrock workflows.
• Drive AI governance with monitoring and safety controls for enterprise adoption.

Skills

Python

PySpark

SQL

Databricks

Scikit-learn

Time Series

NLP

MLOps

Docker

Git/GitHub

AWS

Azure

Kubernetes

Tools

Docker

Git

GitHub

Kubeflow

MLflow

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