Data Scientist (R&D)
In February 2018, we launched our Impact Manifesto, an open and public declaration to our members, staff, and shareholders of our vision and goals, and the strategic moves that we will make to achieve them. Key to these is data science. Our members interact with us and with each other in a myriad of ways, whether it is food and fitness tracking, searching for recipes, shopping in our store, or supporting each other in physical meetings rooms and in our online social network. WW is inherently a data product and we can leverage our data-a massive longitudinal dataset across a wealth of touchpoints and millions of members in different geographies and demographics-and data science to create a more profoundly personalized and impactful experience.
Come join the new and exciting Research & Development team at WW! As a Data Scientist, you will develop predictive and other machine-learned models to enhance and optimize user engagement, help drive the growth of our conversational AI bots, and improve our natural language algorithms while aiding in the development of our experimental projects. Our team of Full Stack Engineers moves quickly and aims to build proof-of-concepts – from ideation to software development to system deployment.
You must be a self-starter who is passionate about working with technology. You should enjoy a team environment where your success depends heavily on the success of your team and organization.
- Partner with stakeholders across the organization to identify high-impact opportunities to leverage our extensive data to better serve our users.
- Develop predictive and other machine-learned models to enhance and optimize user engagement and experience.
- Perform exploratory and targeted analyses to understand the data, the problem, and to generate insights, such as novel features, that can be fed into our models.
- Work with the rest of the team, and with our consumers and partners in dev and product, to implement and ship production-level code.
- Explore and assess utility and potential of additional data sources, APIs, and methods (spanning area such as statistics, deep learning, and natural language processing).
- Motivate and mentor other data scientists to grow their skills and careers.
- 3+ years industry experience building and implementing machine-learned models, ideally dealing with problems relevant to behavior change, community, product, and/or marketing.
- Advanced Degree (Ph.D./MS) in data science, statistics, or a related quantitative discipline.
- Knowledge of Natural Language Processing data models and algorithms.
- Ideally, working knowledge of search, speech, natural language understanding (NLU), natural language generation (NLG), computational linguistics, and symbolics.
- Experience with advanced modeling techniques, such as collaborative filtering and content-based recommenders, matrix factorization, time series analysis, classifiers, natural language processing, and ensemble methods. Bonus points for deep learning.
- Expert knowledge of Python / R and SQL, or similar industry standard tools used for large-scale data analysis and modeling.
- Experience with Google Cloud Platform (Dataflow, Beam, BigQuery, Tensorflow) and other big data technologies a plus.
- Experience working with conversational AI or chat bots. Bonus points for Google's DialogFlow.
- Recent experience with a digital company a plus.
- Self-motivated, results oriented, enthusiastic, and a creative thinker.
Location: New York
We hire only the best people. Here are the benefits to being top-notch:
- The opportunity to work with some of the best innovators in the industry
- Generous healthcare coverage.
- 401(K) with company match.
- Paid Time Off
- Paid parental leave
- Tuition reimbursement
- Annual wellness allowance
- Profit Sharing
WW is an equal opportunity employer. WW does not discriminate on the basis of sex, race, color, creed, national origin, marital status, age, religion, sexual orientation, gender identity, gender expression, veteran status, or disability.
Any offer of employment is contingent upon the satisfactory results of reference and background checks.