Senior Data Scientist - AI Systems for Business Teams

Requirements:

• 6+ years of hands-on experience in applied machine learning or data science, including ownership of production ML systems.
• Strong Python skills and familiarity with common ML and data tooling; experience with platforms such as Airflow, dbt, Snowflake, Spark, or similar.
• Architected and shipped reliable models/services with CI/CD and automated tests; data/feature versioning; canary/shadow releases and safe rollbacks; clear SLOs; monitoring and alerting for drift, latency, and accuracy; retraining pipelines; incident runbooks and on-call practices; and compliance/governance best practices.
• Depth across the ML lifecycle: dataset design, disciplined experimentation, offline and online evaluation, A/B testing, observability, and safe rollout practices.
• Experience integrating model outputs into business systems and measuring impact with business KPIs.
• Comfortable working with both technical and non-technical partners; able to turn ambiguous problems into scoped, testable solutions.
• A product mindset focused on reliability, usability, and measurable outcomes. Bonus: experience writing back to systems like Salesforce or Marketo, or supporting Sales, Customer Success, or customer onboarding conversion workflows.

What You'll Do:

• Design, build, and productionize machine learning systems for revenue-focused use cases such as lead and account scoring, customer onboarding conversion patterns, win/loss signal mining, feature adoption clustering, and recommendations.
• Own projects end to end: problem framing, data sourcing, feature engineering, experimentation, offline and online evaluation, deployment, monitoring, and iteration.
• Define and uphold production-readiness standards: versioned training data, reproducible pipelines, evaluation gates, model and data quality checks, rollback plans, and SLAs.
• Instrument and monitor models in production: drift detection, retraining triggers, performance dashboards, alerting, and post-launch reviews.
• Integrate model outputs into business workflows and systems such as Salesforce, Marketo, Customer Success tooling, customer onboarding systems, product analytics surfaces, and team portals.
• Partner with data engineering and platform teams to use scalable infrastructure for training, serving, scheduling, lineage, and access control.
• Contribute to shared libraries, patterns, and documentation that raise the bar for ML delivery across the org.

Perks and Benefits:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits