Lead Data Science Consultant (MLOps & API Performance Analytics) (W2 or Independent Candidates)

Role: Lead Data Science Consultant (MLOps & API Performance Analytics)

Location: Denver, CO (Onsite)

Duration: Long-term

Note: Need 10+ years of experienced candidates.

About the Role:

We’re seeking a Senior Data Science Consultant with deep experience in Data Science, DevOps/MLOps, and Data Visualization, with an added focus on API performance tracking, analytics, troubleshooting, predictive reliability, and pattern identification.

In this senior role, you will:

• Architect and deliver scalable, production‑grade data and ML solutions
• Lead cross‑functional initiatives to improve system and API reliability
• Build predictive models that forecast failures before they occur
• Guide teams through complex troubleshooting and performance optimization
• Influence technical strategy and engineering standards across the organization
• You will partner with backend engineering, SRE, data engineering, and product teams to deliver high‑impact, data‑driven improvements to stability and performance.

Key Responsibilities:

Data Science & Analytics:

• Lead the design and execution of complex analytical frameworks to detect patterns, anomalies, and failure precursors.
• Conduct advanced EDA to uncover multi‑layer correlations across product, operational, and infrastructure datasets.
• Apply predictive modeling and machine learning to identify where system or API issues are most likely to occur.
• Use statistical process control and drift detection techniques to ensure ongoing operational stability.
• Build simulation and forecasting models to evaluate the impact of load changes, upgrades, or new features on system behavior.
• Establish and enforce best practices for reproducible research, model validation, and experimentation.

API & System Performance Analytics:

• Architect end‑to‑end observability solutions to track API latency, throughput, error rates, saturation, and SLO adherence.
• Build automated pipelines that ingest, aggregate, and model API telemetry logs and traces (OpenTelemetry, Prometheus, CloudWatch, Application Insights, etc.).
• Detect and explain leading indicators of API instability using anomaly detection, time‑series forecasting, and multivariate correlation.
• Provide engineering with “risk heatmaps” to identify high‑risk services, endpoints, or infrastructure components.
• Predictive Reliability & Proactive Mitigation (Senior-Level Additions)
• Design and implement predictive models that forecast outages, SLA breaches, or performance regressions.
• Develop automated early‑warning systems integrated into observability platforms.
• Architect proactive mitigation workflows:
• Adaptive scaling rules
• Automated rollback/canary strategies
• Circuit breakers and fault‑tolerance improvements
• Predictive alerting thresholds

DevOps / MLOps:

• Architect and optimize CI/CD workflows for model deployment and data pipelines.
• Develop and maintain Docker/Kubernetes‑based services for training, inference, and analytics.
• Implement observability frameworks for ML workloads, ensuring traceability, logging, and performance monitoring.
• Maintain model registries, drift detection systems, and automated retraining strategies.
• Use IaC (Terraform/Bicep/CloudFormation) to maintain secure, reproducible environments.

Data Engineering:

• Design and optimize scalable ETL/ELT pipelines across batch and streaming architectures.
• Develop transformations, semantic layers, and feature stores supporting both predictive analytics and operational monitoring.
• Integrate API event logs, telemetry, and performance metrics into high‑quality analytics datasets.
• Establish data quality SLAs and automated validation processes.

Data Visualization & Decision Support:

• Build executive‑quality dashboards that communicate API health, KPIs, predictive signals, and operational trends.
• Create advanced visualizations: forecast bands, anomaly indicators, latency distributions, saturation patterns, and future‑state projections.
• Standardize visualization frameworks, semantic metrics, and documentation across teams.
• Influence decision‑making by translating predictive findings into clear, concise recommendations.

Collaboration, Leadership & Technical Ownership:

• Serve as a technical leader across engineering, driving standards for reliability, observability, and data‑driven decision‑making.
• Mentor engineers and data scientists, conducting code reviews, design reviews, and knowledge‑sharing sessions.
• Lead post‑incident reviews and guide teams in building lasting solutions, not short‑term patches.
• Partner with product and engineering leadership to define roadmaps, set metrics, and prioritize improvements.
• Communicate complex technical topics to executives with clarity and measurable impact.
• Champion a culture of quality, automation, performance excellence, and continuous improvement.

Qualifications:

Required

• Senior‑level proficiency in Python, SQL, and software engineering best practices (testing, design patterns, modular architecture).
• Extensive experience with observability data: logs, metrics, traces, service topology, and distributed systems behavior.
• Hands‑on experience with API performance tools (Grafana, Prometheus, Datadog, New Relic, Splunk, Azure Monitor, CloudWatch, etc.).
• Strong understanding of SLOs, SLIs, latency percentiles, error budgets, traffic analysis, and capacity planning.
• Deep experience with CI/CD pipelines, Git‑based workflows, and automated deployments.
• Strong skills in Docker/Kubernetes and cloud-native microservice environments.
• Expertise in data visualization tools (Power BI, Tableau, Looker) and Python visualization libraries.
• Experience with time-series modeling, anomaly detection, and forecasting (ARIMA, Prophet, Holt‑Winters, LSTM, etc.).
• Proven ability to troubleshoot complex, distributed system issues and drive long‑term resolutions.
• Demonstrated ability to own systems end‑to‑end through design, implementation, deployment, and maintenance.

Preferred:

• Experience in system or API predictive modeling (e.g., Monte Carlo, reliability models).
• Experience building risk scoring systems for performance, stability, or reliability.
• Familiarity with distributed tracing tools (OpenTelemetry, Jaeger, Zipkin).
• Experience with SRE practices and incident‑response engineering.
• Experience with dbt, Airflow, Dagster, or Prefect for orchestration.
• Experience with MLflow, Databricks, SageMaker, Azure ML, or similar MLOps platforms.
• Ability to design automated mitigation strategies (predictive alerts, auto-scaling, failure‑prevention policies).
• Experience influencing cross‑team architecture decisions in large, complex systems.

Success Metrics:

• Reduction in API incidents and faster MTTD/MTTR.
• Improved system reliability: lower error rates, improved latency, higher throughput.
• Successful prediction and prevention of performance degradations before they occur.
• High adoption of dashboards, predictive models, and observability tools.
• Significant improvements in deployment velocity, ML reliability, and platform stability.
• Positive cross-team feedback on leadership, mentorship, and collaboration.