Data Science Lead, Vca Dedicated Team

Visa Visa · Fintech · Tokyo, Japan, Japan

Lead and manage a data science team at Visa, focusing on developing and implementing analytics and consulting services for clients. This role involves hands-on project execution, client interaction, and strategic planning to drive business performance using data science capabilities. The team builds and automates predictive models, assists clients in execution, and innovates new analytics services.

What you'd actually do

  1. Lead, mentor, and manage a team of data scientists, fostering a collaborative and inclusive environment that encourages innovation and continuous learning. Set clear team goals, oversee project execution, and manage team performance.
  2. Develop and implement strategic plans for the data science team, ensuring alignment with the company's objectives. Facilitate communication and collaboration between the data science team and other departments to ensure effective execution of data-driven initiatives.
  3. Hands-on managing and executing multiple medium to large complex analytic projects along with rest of the stakeholders in analytics & consulting teams
  4. Meeting with key clients to explain model results, answer questions and propose further analysis
  5. Executing with appropriate statistical and machine learning techniques

Skills

Required

  • 12 years of analytical experience in applying statistical solutions to business problems
  • Full professional level or above in Japanese and business level English proficiency (or vice versa)
  • Graduate degree (Masters or Ph.D.) in computer science, or a quantitative field such as physics, statistics or mathematics, or equivalent experience
  • Experience in leading and managing a data science team for 2 years or above, with strong abilities to inspire, mentor, and motivate team members while driving projects to successful completion. Excellent interpersonal and communication skills to facilitate cross-functional collaboration are essential.
  • Proficiency in processing large data sets using Hive and related tools
  • Fluency in multiple technologies and languages (Java/C/C++/Python/R/SQL), with experience in at least one compiled language
  • Experience with a wide cross section of machine learning techniques: clustering, dimensionality reduction, variable selection, cross validation, neural networks, gradient boosting, linear & logistic regression
  • Unix/Linux proficiency: command line tools, shell scripting
  • Experience in managing multiple members for at least 2 years
  • Experience in collaborating with business functions at least internally or externally

Nice to have

  • Ability to quickly ideate novel mathematical models to address new problems
  • Experience in cards/payments, retail banking, or retail merchant industries

What the JD emphasized

  • lead, mentor, and manage a team of data scientists
  • Experience in leading and managing a data science team for 2 years or above
  • managing multiple members for at least 2 years

Other signals

  • Develop and implement strategic plans for the data science team
  • Hands-on managing and executing multiple medium to large complex analytic projects
  • Familiarity with batch processing and process monitoring for model refresh
  • Meeting with key clients to explain model results, answer questions and propose further analysis
  • Collaborating closely with key clients to analyze clients’ data, extract insights, and create dashboards
  • Taking the lead in discussing with clients’ data science, corporate planning, and marketing teams to identify opportunities
  • Executing with appropriate statistical and machine learning techniques
  • Providing thought leadership in both using data to solve business problems and arriving at innovative technology and statistical solutions
  • Accountable in ensuring project delivery within timelines and budget requirements
  • Experience in leading and managing a data science team for 2 years or above
  • Proficiency in processing large data sets using Hive and related tools
  • Experience with a wide cross section of machine learning techniques: clustering, dimensionality reduction, variable selection, cross validation, neural networks, gradient boosting, linear & logistic regression
  • Projects you will be a part of: Attrition and propensity modelling, Recommendation systems and clustering algorithms, Network analysis, LTV and forecasting algorithms