Data Scientist

Omnicom Global Solutions; Bengaluru Urban; Hybrid; INR 1,500,000 - 4,000,000; Full time

Job summary

Omnicom Global Solutions is seeking a Data Scientist with 3–5 years of experience to design, build, and operationalize ML models on GCP. You will work on Vertex AI, BigQuery ML, and modern ML frameworks to deliver production-ready AI solutions.

The role requires collaborating with cross-functional teams and supporting a hybrid setup with 3 days in office and 2 days remote work from Bengaluru/Hyderabad.

Qualifications

• 3–5 years of relevant experience as a Data Scientist or ML Engineer.
• Advanced Python with Pandas, NumPy, Scikit-learn.
• Strong SQL with BigQuery / BigQuery ML.
• Hands-on GCP experience (Vertex AI, Cloud Functions).
• TensorFlow and/or PyTorch expertise.
• Kubeflow for ML pipelines (MLOps).
• Production deployment of ML models.
• US shift overlap: 4:00 PM–1:00 AM IST.

Responsibilities

• Design, build, train, and evaluate predictive models and ML algorithms.
• Extract, transform, and analyze large datasets using Advanced SQL and BigQuery.
• Build production-grade ML pipelines on GCP Vertex AI and BigQuery.
• Develop end-to-end ML pipelines with Kubeflow and Vertex AI (CI/CD/CT).
• Write serverless code with Cloud Functions to trigger ML pipelines.
• Collaborate with data engineers, software developers, and business stakeholders.

Job description

Job Title: Data Scientist (3-5 Years Experience)

Shift Timings: 4:00 PM - 1:00 AM IST

Work Model: Hybrid (3 Days Work From Office / 2 Days Work From Home)

Position Overview

We are looking for an experienced Data Scientist with 3 to 5 years of hands-on expertise in building scalable machine learning models and deploying them on Google Cloud Platform (GCP). In this role, you will design, develop, and operationalize end-to-end ML workflows using GCP-native tools like Vertex AI and BigQuery ML, alongside core frameworks like TensorFlow and PyTorch. You will collaborate closely with cross-functional teams to translate business requirements into production-ready AI solutions.

Key Responsibilities

• Model Development: Design, build, train, and evaluate predictive models and machine learning algorithms using Python, TensorFlow, and PyTorch.
• Data Engineering & Querying: Extract, transform, and analyze large datasets using Advanced SQL and BigQuery / BigQuery ML.
• GCP Architecture: Leverage Google Cloud Platform services (Vertex AI, Cloud Functions, BigQuery) to build production-grade ML pipelines.
• MLOps & Automation: Build and maintain automated end-to-end ML pipelines using Kubeflow and Vertex AI for continuous integration, training, deployment, and monitoring (CI/CD/CT).
• Serverless Workflows: Write and deploy serverless code using Cloud Functions to trigger ML pipelines and handle event-driven data processing.
• Cross-Functional Collaboration: Partner with data engineers, software developers, and business stakeholders to integrate ML solutions into existing applications.

Required Skills & Qualifications

• Experience: 3-5 years of relevant experience as a Data Scientist or Machine Learning Engineer.
• Core Programming: Advanced proficiency in Python and deep knowledge of data manipulation libraries (Pandas, NumPy, Scikit-learn).
• Database & SQL: Strong expertise in Advanced SQL (complex joins, window functions, query optimization) and experience working with BigQuery / BigQuery ML.
• Cloud Platform: Proven hands-on experience with Google Cloud Platform (GCP), specifically Vertex AI and Cloud Functions.
• ML Frameworks: In-depth experience with TensorFlow and/or PyTorch.
• MLOps & Pipeline Tools: Practical experience using Kubeflow to orchestrate machine learning workflows and manage model lifecycles in production.
• Problem-Solving: Strong analytical and algorithmic skills with a track record of deploying robust ML models into live environments.
• Shift Schedule: US overlap coverage requiring shift timings from 4:00 PM to 1:00 AM IST.
• Office Location: Flexible hybrid presence required out of either Bangalore or Hyderabad offices (3 days WFO / 2 days WFH).