Director, Data Science (AI Engineering)

Blend360•Columbia, MD

About The Position

We are seeking a visionary and execution-oriented Director of AI Engineering to work with one of our largest accounts. In this senior, client facing role, you will own the full lifecycle of AI model development-setting technical strategy and ensuring that cutting-edge machine learning solutions move from concept to production with business impact at their core. With roots in data science and hands-on expertise in custom transformer architecture, you will bring both the credibility to lead technical teams and the strategic mindset to align AI investments with client outcomes. You will operate at the intersection of stakeholder management and deep technical execution - equally comfortable presenting to a senior audience and reviewing model architecture with your team. This is a high-impact, high-autonomy role with significant organizational influence. You will define the AI roadmap, establish engineering best practices, and champion a culture of rigorous, reproducible, and responsible machine learning.

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.

Nice To Haves

Experience across multiple client engagements or business units—including headcount planning, tooling standardization, and cross-team AI governance.
Background in privacy-preserving ML: federated learning, differential privacy, or synthetic data generation—especially relevant in post-cookie marketing environments.
Hands-on experience with marketing mix modeling (MMM), media attribution, CLV modeling, or subscriber lifecycle optimization at enterprise scale.
Knowledge of graph neural networks (GNNs) for social graph or network topology analysis in telecom contexts.
Published research or conference contributions (NeurIPS, ICML, KDD, RecSys, or industry equivalents) related to applied transformers, tabular deep learning, or domain-specific AI.
Experience with real-time inference and streaming ML pipelines (Kafka, Flink, or similar).
Demonstrated ability to build strategic partnerships with external clients, contributing to revenue growth or account expansion through technical leadership.
Deep experience with openai focused on embeddings
Experience building custom transformer models

Responsibilities

Define technical investments with business objectives
Mentor, and manage f AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations, career development paths, 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 in marketing, media, and telecommunications—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.