Sr. Director, Data Science

Overview

Cadent connects the TV advertising ecosystem. We help advertisers and publishers identify and understand audiences, activate campaigns, and measure what matters – across any TV content or device. Aperture, our converged TV platform, simplifies cross-screen advertising through a streamlined workflow that brings together identity, data, and inventory with hundreds of integrated partners. For more information, visit cadent.tv.

 

Right now we are looking for a highly motivated and experienced Senior Director of Data Science within the Data Services Organization who will be responsible for shaping our data science strategy, managing a team of data scientists, delivering ML products and collaborating with cross-functional teams to translate data into actionable business strategies. The Sr. Director will apply scientific methods to identify business optimization strategies and develop, evaluate, and demonstrate prototypes and production grade builds.

 

Data Scientists collaborate directly with the Business, Product, Data Engineering, DevOps and QA team members to productize AI/ML research to drive business growth. This is a critical role that needs knowledge of mathematics and engineering, output from this role will be leveraged by business, engineers and by senior executives to define the future of Cadent.

Responsibilities

• Lead, mentor, guide and manage data science team & junior data scientists providing guidance on technical and professional growth and develop a team of data scientists
• Establish roadmaps, deliver projects end to end ML model builds including deployment & monitoring
• Manage resource allocation, project prioritization, and workload distribution among team members
• Define project scope, objectives, and success metrics, ensuring projects are delivered on time and within budget
• Oversee the end-to-end lifecycle of data science projects, from problem formulation to model deployment and monitoring
• Hands-on design, train and apply statistics, mathematical models, & machine learning techniques to create scalable ML solutions such as identity to solve business problems and build ML data products to enable speed to market and rapid experimentation
• Strive to innovate leveraging latest algos, tools, data and systems while staying abreast of industry trends and best practices in data science
• Work with machine learning engineers, data engineers, DevOps and software developers to deploy models and modeling pipelines to be leveraged by business
• Align with Product and business on project deliverables, timelines, provide updates on progress
• Foster a collaborative and innovative team culture that encourages knowledge sharing and continuous learning
• Leverage model governance techniques and frameworks to ensure performance and stability of data science products
• Participate in the Agile / scrum process
• Follow the CRISP-DM process to generate robust documentation associated with iterative work
• Present results and findings to technical audience, product and business stakeholders
• Collaborate effectively with broader data services group including but not limited to Machine Learning Engineers, Data Engineers, Analytics Engineers, Software Engineers, Quality Assurance Engineers, and Business Intelligence analysts
• Encouraging team to participate in researching new data, tools, algorithms and tech stack to align with evolving AI & ML industry

Qualifications

• M.S. or higher in computer science, mathematics, operations research, statistics or related discipline with a focus on machine learning; or the equivalent of 10 -12 years’ experience in a Data Science and Machine learning role
• 8+ years of data science and machine learning developer hands on keyboard experience
• 5 – 6 years of managerial experience leading medium (7-10) Data Science teams and spear heading data science projects coordinating with business & product
• Experience working with LLM technologies, including developing generative and embedding techniques, modern model architectures, retrieval-augmented generation (RAG), fine tuning / pre-training LLM (including parameter efficient fine-tuning), and evaluation benchmarks
• Proven background answering open ended research questions using data, tools and technology
• Ability to write clean, expressive code in Python, use of open source frameworks such as TensorFlow, PyTorch, scikit learn) and or other tools including PySpark, Scala etc.
• Experience with SQL and reading from relational databases, experienced using cloud computing ecosystems (e.g., AWS, GCP)
• Experience with the practical application of computational statistics and complex ML algos including deep learning, GraphDB SVMs, time series forecasting etc. to b