Job Description

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 it’s 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 products—experimenting with new features, validating hypotheses, and shipping ML- and LLM-powered capabilities that drive commercial outcomes. You’ll 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



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