- Company Name
- FanDuel
- Job Title
- Director of Machine Learning Engineering
- Job Description
-
Job title: Director of Machine Learning Engineering
Role Summary:
Lead and scale the development, deployment, and operationalization of advanced machine learning systems across the organization, driving measurable business outcomes through reusable ML services. Oversee strategy, talent, and cross‑functional collaboration to ensure production‑grade models support key decision points and customer experiences.
Expectations:
- Deliver end‑to‑end ML solutions that are highly available, secure, and cost‑efficient.
- Partner with data science, engineering, and product leaders to prioritize high‑impact use cases.
- Cultivate a culture of experimentation, reproducibility, and continuous improvement.
- Exhibit strong communication and influence skills across technical and non‑technical stakeholders.
- Manage competing priorities in a fast‑paced, evolving environment.
Key Responsibilities:
- Define and execute the ML services strategy, aligning with business goals and customer needs.
- Build a high‑performance ML engineering team: hire, mentor, and retain top talent.
- Design and implement scalable ML infrastructure, including model lifecycle management and reusable components (APIs, pipelines).
- Drive MLOps practices: experimentation, training, inference, monitoring, and continuous learning.
- Standardize governance, performance monitoring, and retraining workflows.
- Act as a bridge between data scientists, ML engineers, platform engineers, and product teams.
- Translate ML capabilities into stakeholder‑friendly language and business value statements.
- Improve development workflows for productivity, observability, delivery speed, and quality.
- Partner with data governance, privacy, and security teams to ensure compliance.
Required Skills:
- 8+ years in data science, machine learning, or data engineering; 3+ years in technical leadership/management focused on ML.
- Proven experience building robust, scalable ML pipelines and platforms.
- Expertise in modern ML and data technologies (Spark, Airflow, Kubeflow, Databricks, Kafka, TensorFlow/PyTorch, Feast, AWS ML services, etc.).
- Hands‑on experience with cloud platforms (AWS, GCP, Azure).
- Strong record of bringing models from prototype to production and optimizing performance.
- Excellent communication, influence, and stakeholder management skills.
- Ability to lead high‑performing teams and handle competing priorities in a dynamic environment.
Preferred Skills:
- Experience supporting data‑driven product or customer‑centric teams.
- Familiarity with ML product thinking and advanced analytics pipelines.
- Background in B2C, eCommerce, or digital environments.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related field.
- Professional certifications (e.g., AWS Machine Learning Specialty, GCP Professional Machine Learning Engineer) are advantageous.