Data Scientist

Location: India

Department: R&D

Workplace: remote

Employment Type: full

Description

Company Summary

Irth Solutions is a software product company building cutting-edge technology platforms that set industry benchmarks across Damage Prevention, Asset Integrity, Land Management, and Stakeholder Engagement. With a strong product culture, collaborative environment, and high growth potential, Irth offers opportunities to work on modern, enterprise-scale data and AI platforms.

Irth is building a unified, governed Databricks Lakehouse to centralize data and power analytics, AI/ML, and customer-facing data products across AWS, Azure, and GCP.

Job Summary

As a Data Scientist, you will play a hands-on role in developing, deploying, and scaling machine learning and GenAI solutions on Irth’s enterprise Lakehouse platform.

You will work closely with data architects, engineers, and product teams to transform data into actionable insights and production-ready AI solutions. This includes building predictive models, deploying GenAI applications, and enabling data-driven decision-making across Irth’s product ecosystem.

Requirements

Primary Responsibility

Lakehouse & Data Foundations

·       Contribute to medallion architecture pipelines (Bronze, Silver, Gold) and ensure data quality and governance.

·       Support lineage tracking, data contracts, and governance processes using Unity Catalog.

·       Apply data residency and governance policies, ensuring secure and compliant handling of sensitive data.

ML & GenAI Model Development

·       Develop and deploy machine learning models including forecasting, anomaly detection, NLP, and predictive analytics.

·       Design and implement GenAI solutions such as RAG pipelines, LLM-powered assistants, and intelligent automation.

·       Package and manage models using Unity Catalog and model registries.

·       Collaborate with product teams to define KPIs, conduct experiments, and productionize successful models.

Scalability, Reliability & Performance

·       Build scalable workflows using Databricks, Spark, and cloud-native tools.

·       Implement monitoring, model evaluation, and performance tracking to ensure reliability.

·       Apply cost optimization and performance tuning best practices.

Data Product & Analytics Enablement

·       Support enterprise semantic layers for BI tools such as Power BI and Databricks AI/BI.

·       Collaborate with engineering and product teams to enable scalable, production-ready data products.

·       Document models, pipelines, and analytical workflows.

Security, Compliance & Governance

·       Implement access control policies (RBAC/ABAC), secure data access, and compliance controls.

·       Support regulatory compliance including SOC 2, GDPR, ISO 27001, and PIPEDA.

·       Maintain documentation supporting governance, audit, and disaster recovery requirements.

Skills & Experience

Required:

·       3–6 years of experience in Data Science, Machine Learning, or applied AI roles.

·       Strong experience with Python, SQL, and distributed processing frameworks such as Spark.

·       Experience with Databricks, Delta Lake, and ML model deployment workflows.

·       Experience with ML lifecycle including feature engineering, model training, evaluation, and monitoring.

·       Hands-on experience with GenAI technologies including prompt engineering, RAG pipelines, and LLM integration.

·       Experience with CI/CD pipelines, version control, and automated deployments.

·       Strong understanding of data governance, security, and data quality practices.

·       Strong analytical thinking, problem-solving, and communication skills.

Preferred:

·       Experience with Azure (ADLS, Entra ID, Power BI) or AWS (S3, Secrets Manager, KMS).

·       Experience with MLflow, streaming pipelines, and model serving.

·       Knowledge of geospatial analytics, infrastructure, or utility domain data.

·       Experience with observability, monitoring, and performance optimization tools.

·       Relevant cloud or Databricks certifications.

Success Metrics

·       Successful deployment of ML and GenAI models into production.

·       High-quality, reliable pipelines with strong governance and lineage compliance.

·       Improved product capabilities through data-driven insights and AI solutions.

·       Strong collaboration with engineering, product, and architecture teams.

·       Compliance with enterprise security, governance, and performance standards.

EDUCATION

·       Bachelor’s or master’s degree in computer science, Software Engineering, or a related field, or equivalent professional experience.

Benefits

WHAT IS IN IT FOR YOU

·       Being an integral part of a dynamic, growing company that is well respected in its industry.

·       Competitive pay based on experience.