Data Scientist (GCP Cloud)

Job Title: Data Scientist (GCP Cloud)

Experience: 6+ years

Work Timings: 2:00 PM – 10:00 PM IST

Location: Remote

Job Overview:

We are seeking an experienced Data Scientist with strong expertise in Google Cloud Platform (GCP) to design and deploy advanced machine learning models at scale. The ideal candidate should have hands-on experience working with GCP’s suite of cloud services, implementing ML pipelines, and optimizing data-driven solutions to support business objectives.

Key Responsibilities:Data Science & Modeling:

• Design and develop predictive analytics and decision-support models.
• Apply advanced statistical techniques, NLP, computer vision, and time series forecasting.
• Create and optimize data pipelines and ETL workflows for efficient model training and deployment.

Cloud Implementation (GCP):

• Deploy and manage ML models using Vertex AI, AI Platform, and BigQuery ML.
• Build scalable data pipelines leveraging Cloud Dataflow, Pub/Sub, and Cloud Composer (Airflow).
• Utilize Google Cloud Storage, BigQuery, and Cloud Functions for seamless data integration and management.

Collaboration & Strategy:

• Work closely with data engineers, cloud architects, and business teams to align models with business objectives.
• Translate complex data insights into actionable recommendations for stakeholders.
• Ensure scalability, performance, and security of cloud-based data solutions.

Required Skills & Experience:

• 6+ years of experience in Data Science and Machine Learning.
• 3+ years of hands-on experience with Google Cloud Platform (GCP) services.
• Strong proficiency in Python, SQL, and TensorFlow/PyTorch.
• Hands-on experience with BigQuery, Vertex AI, Cloud ML Engine, and Dataflow.
• Practical knowledge of MLOps tools like Kubeflow, MLflow, or CI/CD pipelines.
• Solid understanding of cloud security, IAM, and service accounts.

Preferred Qualifications:

• GCP Professional Data Engineer or Machine Learning Engineer certification.
• Experience with APIs and microservices for model deployment.
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Knowledge of A/B testing, hyperparameter tuning, and model interpretability techniques.