Data Scientist

Company Description

OUR STORY:

Equinox Group is a high growth collective of the world's most influential, experiential, and differentiated lifestyle brands. We restlessly seek what is next for maximizing life - and boldly grow the lifestyle brands and experiences that define it. In addition to Equinox, our other brands, SoulCycle and Equinox Hotels are all recognized for inspiring and motivating members and employees to maximize life. Our portfolio of brands is recognized globally with locations within every major city across the United States in addition to London, Toronto, and Vancouver. 

OUR CODE:

We are passionate about high performance living and we practice what we preach – investing time in our own health and fitness. We believe that everyone has untapped potential within them and it takes a disruptive approach to unleash it. We dream big and don’t settle for the status quo. We sweat the details. We never accept less than 110% to help each other deliver the Equinox experience and enable our members to get great results.  We are obsessed with what’s new, what’s now, what’s next. Never following, always leading, and living ahead of the moment in fashion, culture, and consumer behavior. We aren’t just a company; we’re a community vested in each other’s success. We value humility and a team approach at every level of the company.

If you are a high performing individual who is passionate about winning and inspiring others then we are excited to discuss career opportunities with you.

Job Description

We are seeking an experienced Data Scientist to join our team in developing and implementing advanced analytics solutions and recommendation systems.

This role will focus on creating forecasting and classification models to drive business decisions, particularly in the domains of sales optimization and customer retention. As a Data Scientist on our team, you will be responsible for developing and maintaining a diverse portfolio of machine learning models, including classification, forecasting, and regression systems for critical business applications.

A key focus of this role will be creating and implementing propensity models to help optimize our conversion funnels. You'll also build predictive models for attrition analysis and lifetime value estimation.

The role involves extensive data exploration and analysis to support various business initiatives, working in close collaboration with our Sales, Marketing and Operations teams to understand and meet their requirements. You'll focus on automating and productionizing machine learning pipelines to ensure scalable solutions while also designing and executing experiments to measure them. Your expertise will be instrumental in driving data-driven decision-making processes across the organization.

You will also be heavily involved in building content recommender systems, generative AI applications, A/B testing, natural language processing, data product development, and data platform development. Experience with developing and deploying data pipelines, software development, writing unit tests, git, CI/CD, and DAG tools will be valuable skills towards these initiatives.

You will work closely with fellow data scientists, analysts, data engineers, and product managers on the broader tech team. This role reports to the Head of Data Science.

Qualifications

• A degree in a computational field (Ph.D. preferred)
• 3+ years of experience in Data Science, ML Engineering or related field
• Expertise in building models to aid business forecasting and decisions (e.g., lifetime value, churn, seasonality, likelihood to convert, utilization, pricing)
• Expertise with optimization algorithms and problem formulation
• Expertise in measuring predictions via A/B tests and other experiments
• Exceptional Python experience as well as common libraries for data exploration, data cleaning, machine learning and statistical analyses
• Advanced SQL
• Interest in aiding major business decisions across sales, marketing and operations

Experience in the following areas is preferred:

• Databricks
• Recommender systems
• Natural Language Processing
• Cloud infrastructure (we work on AWS and deploy infrastructure using Terraform. Common services we user are Athena, Redshift, Lambda, Dynamo, S3, API Gateway, CloudWatch, Bedrock, Amazon Personalize)
• Event-driven and batch data pipeline architectures
• Workflow orchestration tools (we use Prefect and Rundeck)
• Version control (we use GitHub)
• CI/CD build and deployment tools (we use Travis and Jenkins)
• Writing unit