VP of Data Science

I. Job Title

VP of Data Science

II. Job Overview

The VP of Data Science is responsible for developing the company's data science strategy, leading the data science team, and driving business growth and decision optimization through machine learning, statistical modeling, and data analytics. This role requires translating business problems into data solutions and driving the productization and large-scale deployment of data.

III. Core Responsibilities

• Strategy and Leadership

Develop and execute the company's overall data science strategy, aligning it with the company's business objectives.

Report to the CEO/CTO and participate in senior management decision-making.

Build and manage a high-performing data science team (data scientists, ML engineers, analysts, etc.).

Drive the adoption of a data-driven culture within the organization.

• Data Science and AI Applications

Lead projects in machine learning, deep learning, and predictive analytics.

Build key models (e.g., user growth, risk control, recommendation systems, pricing models, etc.).

Drive the application of AI in core business scenarios (e.g., marketing, operations, supply chain, healthcare decision-making, etc.).

Supervise model development, validation, deployment, and continuous optimization (MLOps framework).

• Data Products and Commercialization

Promote the productization of data capabilities (e.g., data platforms, algorithm services, BI tools).

Collaborate with product teams to transform data capabilities into actionable products or features.

Uncover the commercial value of data (monetization, efficiency improvement, cost optimization).

• Cross-Departmental Collaboration

Work closely with engineering, product, business, and finance teams.

Transform complex data results into understandable business insights.

Support the data needs of marketing, sales, and operations departments.

• Data Governance and Compliance

Establish data standards and data quality management systems

Ensure data security and privacy compliance (e.g., GDPR, HIPAA)

Manage data architecture and data assets

IV. Job Requirements

• Education Background

Master's degree or above in Computer Science, Statistics, Mathematics, Data Science, or related fields (PhD preferred)

• Work Experience

Over 20 years of data-related experience, including over 5 years of team management experience

Experience in implementing large-scale data platforms or AI projects

Experience in the internet, finance, healthcare, and retail industries is preferred

• Professional Skills

Proficient in machine learning, statistical modeling, and data mining

Familiar with data tools such as Python/R/SQL

Familiar with big data technologies (e.g., Spark, Hadoop)

Understand model deployment and MLOps processes

• Management and Business Abilities

Strong strategic thinking and business acumen

Excellent team leadership and organizational skills

Strong cross-departmental communication and implementation skills

Able to translate data insights into business value