Data Science Product Owner

We are Vericast. We create meaningful connections between business and the people that they serve-how, when and where it matters. By pushing the boundaries of data and insights, we spark discovery and inspire action to create profitable results.

At Vericast, data, analytics, and technology are at the heart of our strategy. Data Science plays a crucial role in delivering our solutions today and will play a more prominent role in our future. As we look at expanding our data science teams (growing at over 100% YOY), we want to make sure that we support this team with a Data Science Product Owner. Today, data science at Vericast has a machine learning engineering and modeling component. While machine learning engineering follows a traditional software development life cycle (SDLC), modeling depending on the final consumption of the output, may not follow a conventional SDLC.

The Data Science product owner will help the data science teams bridge the gap between product and business stakeholders who request Data Science support and the teams that deliver.

Data science projects have a mathematical foundation, an exploratory dimension, and a data-driven workflow, and these tend not to fit nicely into a traditional product development lifecycle. The Data Science Product Owner will be responsible for:

• 
  Managing and prioritizing the product backlog.
• Translating product managers' strategies to tasks for the data science teams.
• Serving as a liaison between product and development.
• Staying accessible to development to answer questions.

Additionally, the Product Owner will work with the Data Science team to support the following areas:

• Feasibility assessment: Streamlining the interface between R&D processes (feasibility assessment, exploration, literature review…) and production.
• Scientific approach: Integrating the necessary steps for implementing scientific approach in product development (observation, formulation of problems, hypothesis testing, phenomenon modelling, experimentation, evaluation…)
• Intermediate delivery: Designing intermediate delivery adapted to the experimental nature of data science workflows which do not necessarily result in a deployed feature. This is critical as data science projects are by nature iterative. Being able to delivery incremental value for the business to validate is critical to drive a good test and learn discipline in Data Science.
• Complex estimation: Estimating efforts and charges for deploying a Data Science feature may be complex due to the exploratory phase and dependency to data availability and quality. The PO needs hands-on experience to better estimate features value and complexity and integrate them to the development cycle.
• Data-driven: The PO must know very well the input data (quality, availability, formats, source, etc.). Manipulating the data helps for better anticipating constraints and potential improvements of the delivered solution.
• Modelling: The Data Science PO should master the Machine Learning pipeline (model designing, testing, training, validating, serving…) and be able to explain model weakness for guiding the product roadmap to the most effective improvements.
• Validation: The validation of data-driven features requires skills in data wrangling, use of notebooks to standardize tests, and a good understanding of evaluation metrics, etc.
• Presentation: Consult with the business to contextualize and translate the results of our analysis in a form which the business can understand and act on. This includes written reports, presentation, and data visualizations, and draws clear lines between the high-level problem specifications, the analyses performed, and how the results link directly back to business objectives. It requires the incumbent to demonstrate their depth of understanding of many areas of the business, the data, and the analytical methods through ongoing engagement with a range of audiences.

Skills:

• At least one of the following automation tools and techniques: Python/Java automation libraries, Knowledge of Python, Java, R, and/or Scala, Proficiency with big data tools such as Hadoop, SparkMap Reduce, Pig, Solr
• Proficient operating in a Linux environment and with basic shell scripting
• Proficient in SQL
• Demonstrated knowledge of concepts of power analysis, hypothesis testing, inference
• Strong statistical background and preferably a good knowledge of data mining techniques
• Strong Statistical Analysis, Written & Oral Presentation Skills
• Collaborative Problem Solving, Data Analysis

Experience

• Minimum 3 years’ experience in a product management role requiring setting priorities and defining new products
• Experience implementing Data-Science-related products (Recommendation Systems, Learn-to-Rank, etc.)
• Experience with A/B or Multivariate testing.
• Experience with the SCRUM methodology is a plus, but not required
• Minimum of one year in using large datasets, statistical software and Data Visualization technologies

Vericast offers a generous total rewards benefits package that includes medical, dental and vision coverage, 401K matching and flexible PTO. A wide variety of additional benefits like life insurance, employee assistance and pet insurance are also available, not to mention smart and friendly coworkers!

At Vericast, we don’t just accept differences - we celebrate them, we support them, and we thrive on them for the benefit of our employees, our clients, and our community. As an Equal Opportunity employer, Vericast considers applicants for all positions without regard to race, color, creed, religion, national origin or ancestry, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, or any other classifications protected by law. Applicants who have disabilities may request that accommodations be made in order to complete the selection process by contacting our Talent Acquisition team at talentacquisition@vericast.com.

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