URGENT HIRING / Data Scientist / 2-5 years experience / Pune / Immediate - 15 days notice preferred

About the job

About Senzcraft:

Founded by IIM Bangalore and IEST Shibpur Alumni, Senzcraft is a hyper-automation company. Senzcraft vision is to Radically Simplify Today's Work. And Design Business Process For The Future. Using intelligent process automation technologies.

We have a suite of SaaS products and services, partnering with automation product companies.

Please visit our website - for more details

Our AI Operations SaaS platform

Senzcraft on linkedin ->

Senzcraft is awarded by Analytics India Magazine in its report State of AI in India as a Niche AI startup. Senzcraft is also recognized by NY based SSON as a top hyper-automation solutions provider.

About the Role (Data Scientist) :

We are looking for a Data Scientist to design experiments and productionize machine learning and generative AI solutions on Azure , with a strong focus on measurable business impact, reproducibility, and MLOps best practices .

You will transform sales and stakeholder needs into impactful data productsexperimenting with new features, validating hypotheses, and shipping ML- and LLM-powered capabilities that drive commercial outcomes. Youll balance fast iteration with enterprise-grade data, MLOps, and Responsible AI standards .

Key Responsibilities

• Explore, prototype, and validate new features , from hypothesis to measurable business outcomes
• Translate sales and stakeholder needs into analytical problems, KPIs, and success metrics
• Build, evaluate, and iterate ML models for recommendations, scoring, forecasting, and insights
• Design robust data ingestion, feature engineering, and training pipelines using Azure ML and Microsoft Fabric
• Productionize models with CI/CD, versioning, monitoring, telemetry, and rollback using Azure DevOps
• Leverage LLMs (prompt engineering, RAG) to enhance the sales copilot; define guardrails and evaluation strategies
• Ensure data privacy, security, and compliance across the full ML lifecycle
• Monitor model performance, drift, and cost; run A/B tests and incorporate user feedback
• Collaborate with Product and Engineering teams to deliver incremental value in agile sprints
• Document design decisions, share knowledge, and contribute reusable components and templates
• Integrate ML solutions with CRM, BI tools, and enterprise data sources
• Mentor interns and junior team members; promote best practices in Data Science and MLOps