Technical Program Manager

1.  Compute infrastructure and orchestration

One of the biggest challenges was compute scale. The existing CPU-based setup could not process large data science workloads or model signals quickly enough to support a user base of more than 120 million people.

• ·Infrastructure foundation: I helped scale a cloud-based platform on AWS, managed through Kubernetes, so the team had a more stable and flexible environment for production workloads.
• ·GPU scaling: We deployed an 80+ GPU cluster to handle the heavy parallel processing needed for model training and inference at scale.
• ·Cost and utilization: We automated scheduling and resource management so unused capacity could be reclaimed quickly. That increased hardware utilization by 65% and delivered about $4M–$6M in infrastructure savings per publisher.

2.  Data platform and pipelines

We also had to replace slow, manual data handoffs with repeatable pipelines that could move large volumes of user and publisher data into the models in a reliable way.

• ·AWS-native data platform: We used Amazon S3 as the core data layer, with AWS Glue and EMR providing large-scale transformation and feature preparation to create production-ready datasets for analytics and model execution.
• ·Automated pipelines: I built automated ingestion and validation pipelines using AWS Glue, EMR, and Redshift so raw behavior, campaign, and publisher data was cleaned, standardized, and ready before it reached the scoring models.

3.  Governance and downstream activation

Finally, we had to make sure the solution could meet security and privacy expectations without slowing the program down.

• ·Built-in security controls: I embedded compliance checks into the delivery pipeline so encryption, data isolation, and privacy approvals were validated before data moved into production workflows.
• ·Marketing activation: I exposed model outputs through resilient APIs so downstream marketing systems could use scores and audience segments in near real time.
• ·Visibility and experimentation: I set up data lineage and operational dashboards so marketing teams could see performance clearly and use the outputs confidently in ongoing testing and optimization.

4.  Model testing before production

Before any model went live, I put it through a structured validation process to make sure it would perform well, stay stable, and hold up under real production conditions.

• ·Offline validation: We tested models on holdout data [a portion of data kept separate from training so the model can be evaluated on unseen examples] and out-of-time data [data from a later time period used to check whether the model still performs well as conditions change], and compared them with baseline approaches using AUC, precision, recall, lift, and false-positive rate to confirm they were materially better before moving forward.
• ·Shadow testing and pilots: Before full launch, we ran the models in shadow mode [running the model on live or production-like data without using its outputs to make real decisions] and limited pilots to check score stability, latency, throughput, error rates, and business lift under production-like conditions.
• ·Data and pipeline checks: I verified schema consistency, freshness, feature distributions, and scoring parity across environments, and monitored drift indicators like PSI [population stability index, which measures whether input data distributions have shifted enough to risk model performance] to catch issues before launch.
• ·Launch readiness: We used go/no-go reviews to confirm the model met release thresholds for accuracy, calibration [how closely the model's predicted probabilities matched actual outcomes], p95 latency, pipeline reliability, rollback readiness, and monitoring before it went live.

5.  Operational readiness before production

Once the models passed validation, we still had to prove the platform and delivery pipeline could meet production requirements for reliability, security, recoverability, and supportability. Before launch, we implemented operational controls across deployment, observability, access management, incident handling, and downstream dependency readiness so the capability could run under enterprise production standards.

• ·Release controls and rollback: We implemented versioned deployment packages, pre-release validation gates, environment promotion controls, and rollback playbooks so failed releases could be isolated quickly and previous stable versions restored without disrupting downstream activation or reporting workflows.
• ·Observability and service health: We set up dashboards, structured logging, and alert thresholds for job failures, p95 latency, throughput, data freshness, scoring volume, and infrastructure utilization — with service-level expectations and escalation paths so issues could be detected and triaged before they affected business workflows.
• ·Access controls and security hardening: We validated least-privilege IAM roles, encryption at rest and in transit, audit logging, secrets management, and privacy approval checkpoints so production access was controlled, traceable, and aligned with enterprise policy.
• ·Runbooks, incident response, and recovery: We documented failure modes, alert routing, on-call ownership, recovery procedures, and rollback triggers so the team could respond consistently to degraded pipelines, delayed scoring, or downstream publication failures.
• ·Downstream dependency and support readiness: We verified API consumers, activation pipelines, and reporting dependencies were ready for production data contracts, and confirmed support ownership, handoff procedures, and launch criteria so the capability could be sustained after go-live.