Senior Data Scientist, Analytics (Multiple Positions)

Tiktok•San Jose, CA

48d•$275,850 - $329,334

About The Position

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect - and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day. We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us. Our team plays a crucial role in ensuring the company's success. We seek people who are willing to learn and put in the effort to solve problems. Our challenges are not your regular day-to-day problems - you'll be part of a team that's developing new solutions to new challenges. It's working fast, at scale, and we're making a difference. We are looking for talents to join us on this exciting journey!

Requirements

Must have a Master's degree or foreign equivalent degree in Computer Science, Engineering (any), Information Technology, Machine Learning, Data Science, Data Analytics, Finance Analytics, Statistics, Mathematics, or a related field, and 2 years of related work and/or research experience; OR a Bachelor's degree or foreign equivalent degree in Computer Science,
Engineering (any), Information Technology, Machine Learning, Data Science, Data Analytics, Finance Analytics, Statistics, Mathematics, or a related field, and 5 years of post-bachelor's, progressive related work and/or research experience.
Of the required experience, must have 2 years of experience in each of the following:
Developing graphical and visual representations of data using business intelligence tool Excel or Tableau;
Building, tuning, and validating statistical models for regression and classification problems in Python or R;
Conducting exploratory and predictive analysis using Python, R, or Scala programming languages;
Cleaning and processing data using relational databases;
Developing machine learning models in Python; and
Providing data-driven insights and recommendations to mitigate insufficiencies models based on model performance, regulations, or business requirements.

Responsibilities

Develop long-range business forecast models and run scenario analysis to understand the implications of a variety of business decisions on key metrics and share the insights with cross-functional team to influence strategic decisions.
Develop analytic framework in partnership with internal teams to quantify business impact, diagnose issues, and to bring insights using internal and external data.
Define core metrics, build essential data tracking dashboard, and identify opportunities from our database and convert the insights into real changes/new features.
Influence stakeholders through data-based recommendations and mobilize cross-functional teams by telling compelling stories with data to drive business impact and innovative breakthrough.
Conduct experimentations to analyze user behavior and optimize for the best outcome regarding core metrics.
Improve data foundations and excel in product logic thinking using data-driven analysis and brings distinctive problem-solving skills and impeccable business judgment.
Partner with Product, Engineering, User Research, Designer and other teams to solve problems and identify trends and opportunities.
Work with large, complex data sets to solve difficult, non-routine analysis problems by applying advanced analytical methods as needed.

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