Senior Data Scientist, Google Cloud Platform (GCP)

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Job Description:

Senior Data Scientist, Google Cloud Platform (GCP)

A Senior Data Scientist with experience in Google Cloud Platform (GCP) is responsible for leading complex data science projects, building and deploying advanced machine learning (ML) models, and leveraging GCP's ecosystem to drive strategic business decisions. This role requires expert technical skills, strong business acumen, and the ability to collaborate with and mentor other team members. 

Responsibilities

• 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 GCP's 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. 

Qualifications

• Education:
  • A Master's 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. 

Nice-to-Have

• 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. 

Employee Type:
 

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