DIRECTOR - DATA SCIENCE & ANAL

Job Description

Job Description

Overview

What are we building?

Hard Rock Digital is a team focused on becoming the best online sportsbook, casino, and social casino company in the world.

We’re building a team that resonates passion for learning, operating and building new products and technologies for millions of consumers.

We care about each customer's interaction, experience, behavior, and insight and strive to ensure we’re always acting authentically.

Rooted in the kindred spirits of Hard Rock and the Seminole Tribe of Florida, the new Hard Rock Digital taps a brand known the world over as the leader in gaming, entertainment, and hospitality.

We’re taking that foundation of success and bringing it to the digital space ready to join us?

What’s the position?

We are in search of a forward-thinking Director of Data Science to champion our data strategy, engineering, and data science vision.

In this pivotal role, you'll guide our data-centric decision-making, ensuring data precision, dependability, and availability.

You'll also be responsible for advancing modeling techniques, deep learning applications, and innovations in data management and analytics.

This role demands technical depth, robust leadership abilities, and a profound understanding of current data engineering, deep learning, and analytics paradigms.

Responsibilities

Leadership and Strategy :

• Craft and implement the organization's data science vision, ensuring alignment with overarching business aspirations and targets.
• Grow and lead a talented team of data scientists, data engineers, and analysts, promoting a culture of collaboration, discovery and innovation.
• Serve as a thought leader, staying updated with emerging trends, tools, and best practices in data science, including advanced modeling techniques and deep learning.
• Mentor and nurture the data scientists and other data talent building a cohesive, high-functioning team united in the goal of generating value from data.
• Partner with teams across the organization including product, marketing, operations, and data engineering, ensuring that data science initiatives deliver maximum value to the business.

Modeling and Algorithm Development :

• Oversee the end-to-end process of developing, validating, and deploying machine learning and statistical models, ensuring they meet business objectives and drive impactful results.
• Promote cutting-edge techniques and tools, including deep learning, reinforcement learning, LLMs and generative AI to lead in data science applications.
• Implement rigorous monitoring mechanisms for deployed models, ensuring their continued relevance and effectiveness.
• Foster a collaborative environment where data scientists, engineers, and business stakeholders work cohesively to design and refine models that are both technically sound and business-aligned.

Performance Monitoring and Refinement :

• Architect and build processes for monitoring the performance of deployed models both in real-time and batch environments.
• Ensure optimal up time for deployed models, minimizing disruptions and maintaining consistent data-driven decision support.
• Maintain and improve data science modeled alerts for cross functional teams (i.e. real-time fraud detection, customer service chat bots, responsible gaming etc.)
• Develop and contribute to key data engineering and data science initiatives (e.g. feature store, data catalogue).
• Synthesize all model performance indicators to maintain a retraining schedule, understand model drift or feature pipeline disruption, and constantly iterate and improve the model suite.

Cross-functional Collaboration :

• Work with cross function groups to distill their needs into clear and actionable data science project requirements.
• Focus on applications that build value - meet and exceed cross-functional internal stakeholders expectations for how data science can drive value in their area.
• Innovate independently to show other teams the art of the possible’ using data science.
• Create a data science driven organization, one project at a time.

Qualifications

What are we looking for?

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field. A Ph.D. in a relevant domain or an MBA is a plus.
• Demonstrated history of leading and mentoring data science teams in a dynamic, fast-paced environment.
• Strong grasp of statistical modeling, machine learning algorithms, and deep learning frameworks. Extensive experience with data pre-processing and feature engineering.
• Expertise in data science tools and languages such as Python, PySpark, PyTorch, TensorFlow, and Scikit-learn. Strong in SQL, and experienced with deploying models driven by streaming technology (e.g. Kafka + Flink).
• Experience with visualization tools like Streamlit, Matplotlib, Tableau and Power BI to translate complex data insights into understandable visual

Exposure to and interest in :

• Cloud platforms : AWS, Azure, GCP, and their respective ML services.
• Databases : SQL and / or NoSQL (e.g. Cassandra, DynamoDB, Snowflake).
• Streaming and message queues : Kafka, Amazon Kinesis, SQS, RabbitMQ.
• Familiarity with marketing technology such as Segment, Amplitude, Salesforce, or Braze.

Experience with applied data science in the following domains :

• Marketing optimization (MMM, MTA, Customer Segmentation, geo targeting)
• Security, fraud, malfeasance
• Operations automation (customer service, CRM)
• Product personalization
• Comprehensive understanding of data ethics, data privacy, and adherence to compliance standards in machine learning applications.
• Exceptional communication capabilities coupled with strong leadership and the ability to manage stakeholder expectations effectively.
9 days ago