Director, AI Engineering – Data Science

Role Overview

• Define technical investments with business objectives
• Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations and a high-performance culture.
• Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact.
• Establish engineering standards, code review practices, and model governance frameworks across the AI org.
• Serve as the technical authority on deep learning architecture—personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction.
• Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom.
• Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance.
• Champion reproducible experimentation and architectural decision documentation across the team.
• Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment.
• Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks.
• Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs.
• Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives.
• Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence.
• Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice.
• Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC).
• Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a closely related quantitative field; Ph.D. strongly preferred.
• 10+ years of progressive experience in data science and machine learning, with at least 3–5 years in a people management or technical leadership role (Director, Sr. Manager, or Principal Engineer level).
• Proven track record of leading high-performing AI/ML engineering teams in a fast-paced, client-facing or product environment.
• Deep, hands-on expertise designing and training custom transformer architectures from scratch—not only fine-tuning pre-built checkpoints, but architecting novel attention mechanisms, embedding strategies, and model topologies.
• Strong applied data science foundation: feature engineering, statistical modeling, causal inference, and experimental design across large-scale datasets.
• Proficiency in Python and core ML/DL libraries: PyTorch (preferred), TensorFlow, HuggingFace Transformers, scikit-learn, XGBoost/LightGBM.
• Direct experience with industry datasets in marketing & media (DSP/DMP logs, ad impression data, attribution pipelines, MMM) OR telecommunications (CDRs, network KPIs, subscriber behavior, churn datasets).
• Command of SQL and large-scale data platforms: Spark, BigQuery, Snowflake, or Databricks.
• Experience owning end-to-end MLOps: cloud deployment (SageMaker, Vertex AI, or Azure ML), monitoring, CI/CD for ML, and model governance.
• Exceptional executive communication skills—able to translate complex model behavior into business language for C-suite and client audiences.

Tech Stack

• AWS
• Azure
• BigQuery
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes
• Python
• PyTorch
• Scikit-Learn
• Spark
• SQL
• Tensorflow

Benefits

• Flexible work arrangements
• Professional development