Sr. Data Scientist

at  Rekuten Global

Job Description:

SUMMARY:

Rakuten International oversees 7 businesses with over 4,000 employees globally. The brand is recognized for its leadership and innovation in e-commerce, digital content, advertising, entertainment, and communications, bringing the joy of discovery and access to more than 1 billion members across the world.
Our teams deliver on the company’s mission to delight merchants and customers through innovation, optimism, and teamwork.
Rakuten Rewards is a leading e-commerce company that enhances the way people shop by offering Cash Back, deals and rewards from more than 3,500 merchants. Founded in 1999, Rakuten has grown to become the go-to shopping destination for consumers, having paid out nearly $2 billion in Cash Back to its 15 million members. The company also operates ShopStyle, a leading fashion discovery destination, and Cartera Commerce, a top rewards platform for airlines and banks. For more information, visit
www.rakuten.com
.
Do you love solving complex business problems using statistical modeling and machine learning? Do you want to contribute to the transformation of a business at scale, introducing automation and optimization across products and business systems? Rakuten Rewards is looking for a data scientist.
The Data Science team supports a wide spectrum of business domains including user experience personalization (e.g. consumer recommendations), forecasting and optimization of advertising campaigns, anomaly detection/alerting, etc. The scope of our work is broad and still expanding. As a data scientist at Rakuten Rewards, you will have the opportunity to develop and deploy solutions to challenging, practical business problems. Your work will improve the experience of our members and impact the business bottom line.

MINIMUM REQUIREMENTS

• Very strong SQL skills
• Experience with big data and machine learning frameworks. Python/sklearn/pytorch strongly preferred.
• Practical experience with multiple ML problem types (supervised/unsupervised, classification/regression, RL experience is a plus)
• Strong software development skills
• Strong problem solving, communication and collaboration skills
• Firm understanding of statistics and A/B test fundamentals
• Experience with NLP techniques is a plus
• Deep learning/NN experience is a plus

QUALIFICATION REQUIREMENTS

• 5+ years industry experience applying machine learning methods to solve real-world problems
• Bachelor’s degree in AI/ML, computational mathematics, computer science, statistics, physics or related field. Master’s degree strongly preferred.
• Experience in ad-tech and/or consumer services is a plus
  Five Principles for Success
  Our worldwide practices describe specific behaviors that make Rakuten unique and united across the world. We expect Rakuten employees to model these 5 Shugi Principles of Success.
  Always improve, Always Advance - Only be satisfied with complete success - Kaizen
  Passionately Professional - Take an uncompromising approach to your work and be determined to be the best
  Hypothesize - Practice - Validate – Shikumika - Use the Rakuten Cycle to succeed in unknown territory
  Maximize Customer Satisfaction - The greatest satisfaction for our teams is seeing their customers smile
  Speed!! Speed!! Speed!! - Always be conscious of time - take charge, set clear goals, and engage your team
  Rakuten provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type. Rakuten considers applicants for employment without regard to race, color, religion, age, sex, national origin, disability status, genetic information, protected veteran status, sexual orientation, gender, gender identity or expression, or any other characteristic protected by federal, state, provincial or local laws

• Collaborate with manager and stakeholders to specify business problems with precision
• Support the translation of business problems into machine learning problems, including ML problem formulation and evaluation metric specification.
• Perform exploratory data analyses to characterize utility of existing data and to identify needs for new data
• Develop data pipelines for feature computation, model training, inference and evaluation
• Design and execute offline experiments and simulations
• Lead model deployment, business integration and online performance assessment
• Maintain technical documentation, develop/present demos and presentations
• Support management in project planning and status reporting