Senior Data Scientist, Google Cloud Platform (GCP)

End-to-End Project Leadership: Lead and manage the entire data science project lifecycle, from problem definition to model deployment and monitoring.

Advanced Analytics and Modeling:

• Design, develop, and implement advanced statistical and machine learning models to solve complex business problems, such as forecasting, classification, clustering, and optimization.
• Leverage expertise in advanced statistical techniques, including hypothesis testing, regression analysis, and causal inference.

GCP-Specific Responsibilities:

• Architect and build scalable data pipelines and ML model workflows using GCP services such as BigQuery, Dataflow, Vertex AI, and Cloud Composer.
• Develop and manage data warehousing and data lake solutions on GCP, ensuring data quality, integrity, and security.
• Utilize and optimize GCPs machine learning services, including Vertex AI for model training and serving, and BigQuery ML.

Cross-Functional Collaboration:

• Partner with product managers, data engineers, and business stakeholders to translate ambiguous business needs into tractable data science problems and impactful solutions.
• Communicate complex analytical findings and model results clearly and effectively to both technical and non-technical audiences.

Mentorship and Best Practices:

• Provide technical guidance and mentorship to junior data scientists and analysts.
• Advocate for and establish best practices in data science, including code quality, model validation, and responsible AI principles.

Education:

• A Masters degree or Ph.D. in a quantitative field such as Statistics, Mathematics, Data Science, Computer Science, or Engineering is often preferred.

Experience:

• 5+ years of experience in a data science role, with proven experience building and deploying production-level models.
• Extensive, hands-on experience developing and implementing solutions on the Google Cloud Platform.

Technical Skills:

• Programming: Expert-level proficiency in Python and SQL. Experience with PySpark or other big data frameworks is a plus.

GCP Services: Deep, practical experience with core GCP data and ML services, including:

• Data Processing: BigQuery, Dataflow, Cloud Composer
• ML Platform: Vertex AI (including Training, Prediction, and MLOps features)
• Storage: Cloud Storage

• Machine Learning: Expertise in a wide range of ML techniques and algorithms, including regression, classification, clustering, and time series analysis.
• Experimentation: Proficiency in experimental design and analysis, such as A/B testing.
• Visualization: Experience with data visualization tools like Looker, Tableau, or similar platforms

Gen AI

• LLMs : Understand Transformer-based models (GPT, LLaMA, Mistral, etc.), fine-tuning methods (LoRA/QLoRA, RLHF),
• Prompt Engineering : ability to design, optimize, and evaluate prompts for domain-specific use cases, familiarity with techniques like few-shot learning, chain-of-thought prompting, tool augmentation, and agent-based orchestration.
• RAG & Data Integration : Hands-on experience with embeddings, vector databases (FAISS, Pinecone, Chroma DB, Vertex AI Vector Search), retrieval-augmented generation (RAG), and integrating enterprise data sources into GenAI systems

Soft Skills:

• Strong analytical and problem-solving skills, with the ability to tackle complex, ambiguous business problems.
• Excellent communication and presentation skills, with the ability to tell a compelling story with data.
• A collaborative mindset and the ability to influence others to align on resources and strategy.

• Google Cloud Professional Data Engineer or Machine Learning Engineer certification.
• Experience with other cloud platforms (e.g., AWS, Azure).
• Experience in a specific industry, such as retail, advertising, or finance.
• Familiarity with generative AI technologies.