Principal – Data Science Architect

Key Skills

Aartificial intelligence, Apache Hadoop, AWS, Azure, Data Architecture, Data Engineering, Data science techniques, Docker, Google Cloud Platform (GCP), Kubernetes-K8s, PowerBI, Python Programming, PyTorch, R Programming, SPARK Programming, SQL, Tableau, TensorFlow

Job Description

What you will be doing 

The Principal Data Science Architect will be a key technical leader responsible for designing and implementing the data science architecture that underpins our AI and ML modeling capabilities and integration with applications and data pipelines. This person will scale multiple data science models to retail platforms. This role demands a visionary thinker with deep technical expertise and a strategic mindset, who can drive the evolution of our data science infrastructure and methodologies

The position will be based in Pleasant, CA. One of our main regional offices (Seattle, WA, Portland, OR, Pleasanton CA, Denver CO, Dallas TX, Chicago, IL, or Fullerton, CA, Phoenix, AZ). 

 Main responsibilities

Architectural Leadership:

• Define and implement the overarching data science architecture and strategy.
• Ensure the scalability, performance, and reliability of data science solutions.
• Lead the selection and integration of data science tools, frameworks, and platforms.
• Build and deploy data science solutions at scale.

Technical Expertise:

• Design and develop advanced machine learning and statistical models.
• Oversee the deployment of data science models into production environments.
• Ensure best practices in data engineering, model management, and version control.

. Collaboration and Guidance

• Work closely with data engineers, data scientists, and software engineers to build and maintain robust data pipelines.
• Provide technical mentorship and guidance to the data science team.
• Collaborate with cross-functional teams to translate business requirements into scalable data science solutions.

Innovation and Continuous Improvement:

• Stay abreast of the latest advancements in data science, machine learning, and AI technologies.
• Evaluate and implement new tools and technologies to enhance the data science architecture.
• Drive innovation through research, experimentation, and prototyping.

Governance and Best Practices:

• Establish and enforce data science best practices, including coding standards, documentation, and model validation.
• Ensure data security and compliance with relevant regulations and standards.
• Develop and maintain comprehensive documentation of the data science architecture and processes.

The salary range is $ 182,400 to $ 247,000. Starting salary will vary based on criteria such as location, experience, and qualifications. There may be flexibility for exceptional candidates.

 What we are searching for 

 Skills

• Proficiency in SQL and experience with cloud platforms (e.g., AWS, Azure, Google Cloud).
• Proficiency in understanding interaction of multiple data science/ML models.
• Must know how to go from point solutions to platforms
• Strong problem-solving skills and the ability to think critically and analytically.
• Excellent communication and presentation skills.
• Ability to manage multiple projects and prioritize tasks effectively.
• Product mindset and systems thinking

Education: Advanced degree in a STEM field like CS, DS, Engineering, Statistics, Math, etc. is preferred, or equivalent experience from building large scale solutions.

 We believe the successful candidate has these qualifications and experience:

Experience:

• 10+ years of experience in data science, with significant experience in architecting and deploying data science solutions at scale.
• Proven track record of leading and managing data science projects and teams.
• Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data analysis tools (e.g., Python, R).
• Strong background in data engineering and big data technologies (e.g., Hadoop, Spark).

Preferred Qualifications

• Experience in retail industry
• High performance computing background and experience engaging with the engineering teams in a product model
• Knowledge of data visualization tools (e.g., Tableau, Power BI).
• Familiarity with DevOps practices and tools for model deployment and monitoring (e.g., Docker, Kubernetes).