Lead Data Scientist - Recommender Systems

The Global Customer Growth Marketing team’s mission is to drive business growth by keeping our customers at the center. We believe customer behaviors towards the brands they touch are rapidly changing. Digital transformation is touching our lives. Our team uses data, fueled by Artificial Intelligence & Machine Learning (AI/ML), to listen, understand and act so we can give customers what they want– while growing Marketing’s contribution to the business. We are a group of builders – we bring CRM, Data Science, Data Engineering, MLOps and Automation skills together to make the magic happen.

Working with our marketing professionals and marketing database experts, you will build solutions which enable data-driven marketing for our SB businesses across the globe. Your solutions will enhance the quality of leads and product recommendations we deliver to sales and help our marketing teams meet our customer’s needs via optimal campaigns and strategies.

Join us as a Lead Data Scientist – Recommender Systems on our Global Customer Growth Marketing Team in Round Rock, Texas or Remote – USA to do the best work of your career and make a profound social impact.

What you’ll achieve:

You will work with engineers and data scientists to solve some of the hardest business problems, one of them being – building a recommendation engine. Our vision is that the recommendation engine will power all GTM (go to market) channels like web (DellTechnologies.com/Dell.com), inbound (email, ads, display etc) and outbound channels (our sales force etc).

Your analysis will have a direct impact on our outbound sales motion, the campaigns we run and the strategies we employ to serve our customers and drive business outcomes. This role has worldwide focus and you will work as part of a global team – you will need to work within a variety of cultures and time-zones to scale your solutions globally.

You will build strong relationships with our key stakeholders and partners to understand the business needs and pain points. You will use this information to design and drive our recommender systems roadmap, tell stories, deliver insights and accelerate our digital transformation.

You will:

• Building innovative models and solutions – leading discussions to frame problem statements, develop hypotheses, recommend modeling approaches, measure success and establish scalable, efficient, automated processes for large scale neural network based models operating efficiently in production.
• Lead communication with product teams and stakeholders to explain the abstract in simple terms, work closely with our DevOps function to ensure pushing of models to production is successful
• Inspire team members – as one of the senior data scientists on the team, lead the effort to reduce complexity, mentor junior team members, forge creative breakthroughs and operate as ONE Team
• Feature engineering – assist our engineers to develop datasets in a scalable manner for use in algorithms
• Gain a deep expertise on data sources, required transformations, quality, consistency and velocity. Document mapping of all required data resources for modeling

Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:

Essential Requirements

• Proven experience with recommender systems, including applied research and experience with context aware machine learning frameworks 5+ years
• Proven experience with deep learning frameworks such as Tensorflow, Tensorflow Recommenders, Pytorch, MXNet,  Keras
• Demonstrate advanced foundation in Linear Algebra, Calculus and Statistics
• Experience in applying both sequence based and traditional methods to recommender problems – e.g. convolutional neural nets, recurrent neural nets, LSTMs, restricted Boltzmann machines, adversarial networks, collaborative filters, XGBoost, classical clustering techniques
• Experience with data processing software (such as Greenplum, Hadoop, Spark, Pig, Hive)

Desirable Requirements

• Machine learning operations experience, MLFlow, Airflow, Docker, Kubernetes, TFX and experience in Graph database is a Plus
• Proven ability to work with varied data infrastructures – including relational databases, column stores, NoSQL databases, and file-based storage solutions.

Management Level: Individual Contributor