Roles & Responsibilities :

The role will focus on building out machine learning solutions for WBDs Data and Analytics organization.

- Primary focus will be on unlocking machine learning opportunities and building foundational machine learning training and inference pipelines at scale.

- You have a deep understanding of different types of data, metrics and KPIs.

- You will lead by example and define the best practices, will set high standards for the entire team and for the rest of the organization.

- You have a successful track record for ambitious projects across cross-functional teams.

- You are passionate and results oriented.

- You strive for technical excellence and are very hands-on.

- Your co-workers love working with you.

- You have built respect in your career through concrete accomplishments.

- Build cutting-edge capabilities utilizing machine learning and data science (e.g., large language models, computer vision models, advanced ad & content targeting, etc.

- Lead data science and model development techniques for the team.

- Leverage industry best practices and tools to continually improve teams' ability to build machine learning models.

What to Bring :

- BA/BS in statistics, mathematics, economics, industrial engineering, or other quantitative discipline is required.

- Masters/PhD is a plus.

- 8+ years of experience building data science/statistical models (Multivariate regression, Time Series Model, XGBoost, Causal inference etc.

- Strong understanding of modern ML approaches (GBDT, CNN, LSTM, GRU, HRNN, transformers, siamese neural networks, variational auto-encoders, .

- Experience with Deep Learning, NLP, LLMs, Reinforcement Learning, Causal Inference.

- Good knowledge of ML tools and frameworks (TensorFlow, Keras, pyTorch, scikit-learn, Spark,.

- Proficiency in programming languages such as Python or R.

- Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring).

- Good understanding of operating machine learning solutions at scale, covering the end-to-end ML workflow.

- Strong interpersonal skills with the ability to motivate, collaborate and influence.

- Ability to deliver on multiple projects and meet tight deadlines.

- Ability to effectively influence and communicate cross-functionally with all levels.