TIDAL is looking for a Data Scientist to join our growing Analytics team. This role will apply data science to analytics projects and also partner with our growth and retention teams to build models that allow us to optimize acquisition spend, identify customers at risk of churning, and gain a better understanding of customer behavior while implementing marketing solutions to automate processes utilizing the models the data scientist develops.
Key Goals and Objectives:
- Spend Optimization: Create models and algorithms that learn over time to identify high value ad placements / platforms, model incrementality, and optimize efforts across partner, affiliate, organic, and growth acquisition efforts
- Lifetime Value Modeling: Build robust lifetime value models that calculates lifetime value within the short time frame of a user being acquired based on behavioral indicators when using our service
- Identify Moments That Matter: Determine behaviors that identify if a customer is at a high risk of churning or behaviors that indicate loyalty that will allow TIDAL to incent positive customer behavior through marketing activities and product development
- Segmentation and Cohorts: Use statistical modeling to segment our user base based on both internal and external data sources while also creating meaningful cohorts of customers for retention and acquisition campaigns
- Automation: Levering outputs of models, develop processes that will automate daily optimizations on ad platforms, trigger communications, and predict fraud, using APIs of various platforms (ie – Facebook, Snapchat, Google, Adjust, SWRVE, Braze, etc.)
What this role entails/Responsibilities:
- Derive insights and provide recommendations through analyses of large data sets for the organization
- Mine and analyze data from internal databases to drive optimization of acquisition, retention, and content strategy through the development of models and algorithms
- Assess the effectiveness of new data sources and data gathering techniques
- Use predictive modeling to optimize customer experience, revenue generation, ad targeting and retentive triggers
- Develop solutions that facilitate automation across third party platforms
- Develop processes / tools to continuously and improve model performance and data accuracy
- Identify tech tools / tech stack needed to bring to life data science goals
- Derive insights and provide recommendations through analyses of large data sets for the organization
Requirements & Experience:
- 2-5 years of experience manipulating data sets and building statistical models
- A Master’s or PHD in Statistics, Mathematics, Computer Science or another quantitative field
Skills and Abilities:
- Strong problem solving skills with ability to create models using unstructured data
- Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets
- Experience working with and creating data architectures
- Experience with elements of our tech stack: AWS, S3, Spark, Redshift, Postgres, Airflow, Tableau, and Alteryx, Adjust, SWRVE, Braze and other web services
- Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
- Experience with third party APIs and automation development, particularly with ad platforms such as Snapchat or Facebook
- Ability to tell a story using data and insights, and present to non technical users
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
- A self starter with a drive to learn and master new technologies and techniques
- Coding knowledge and experience with several languages