Data Scientist – AI/ML Expert

United State (Remote)
January 25, 2026
Full Time
Urgent

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Job Description

About the Role

At enterprise scale, data science is not an experiment; it is a core operating capability that shapes strategy, products, and competitive advantage. We are seeking a Data Scientist – AI/ML Expert to design, build, and deploy production-grade machine learning systems that power mission-critical decisions across a global organization.

This role is for a practitioner who thrives where models meet reality. You will work on high-stakes problems forecasting demand and risk, optimizing pricing and supply, detecting anomalies and fraud, personalizing experiences, and automating complex decisions using large, imperfect datasets across distributed systems. Your work will move beyond notebooks into robust, monitored, and scalable ML services integrated with enterprise platforms.

Partnering with Engineering, Product, and Executive stakeholders, you will help define the AI roadmap, choose the right modeling approaches, and establish best practices for MLOps, governance, and responsible AI. This is an opportunity to influence how AI is built and trusted at scale, balancing innovation with rigor, speed with reliability, and performance with ethics.

This is not a research-only role and not a reporting role. It is a mandate to deliver measurable business impact through applied AI, end-to-end ownership, and technical leadership.

Essential Duties and Responsibilities

  • Design, train, evaluate, and deploy machine learning models for production use across enterprise platforms.

  • Lead end-to-end ML workflows: problem framing, feature engineering, model selection, validation, deployment, and monitoring.

  • Build scalable pipelines using modern data stacks (cloud data warehouses, streaming, distributed compute).

  • Partner with Product and Business leaders to translate objectives into data science solutions with clear success metrics.

  • Implement MLOps best practices, including CI/CD for models, versioning, monitoring, drift detection, and retraining.

  • Apply advanced techniques (e.g., gradient boosting, deep learning, NLP, time-series, causal inference) where appropriate.

  • Ensure model fairness, explainability, and compliance with enterprise governance standards.

  • Optimize models for performance, latency, cost, and reliability in production environments.

  • Mentor other data scientists and influence standards across the data science community.

  • Communicate findings, trade-offs, and recommendations clearly to technical and non-technical audiences.

Job Qualifications and Requirements

  • 7–10+ years of experience in data science, applied ML, or AI engineering within enterprise or platform environments.

  • Strong foundation in statistics, machine learning, and optimization.

  • Demonstrated experience deploying ML models to production at scale.

  • Proficiency with Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost).

  • Experience with cloud platforms (AWS, Azure, or GCP) and distributed data systems.

  • Hands-on experience with MLOps tools and practices.

  • Ability to work across ambiguous problem spaces and deliver outcomes.

  • Strong communication skills with experience influencing senior stakeholders.

Personal Capabilities and Qualifications

  • Systems thinker who understands how models behave in real-world environments.

  • Curious, rigorous, and disciplined balances experimentation with engineering excellence.

  • Comfortable owning problems end-to-end and accountable for results.

  • Collaborative partner who builds trust across Engineering, Product, and Business teams.

  • Pragmatic decision-maker who chooses the right solution, not the fanciest one.

  • Strong ethical compass and commitment to responsible AI.

Strategic Support

  • Advise leadership on AI opportunities, trade-offs, and investment priorities.

  • Support enterprise AI strategy, platform standardization, and tooling decisions.

  • Partner with Security, Legal, and Compliance on model governance and risk management.

  • Contribute to long-term data and AI roadmaps that align with business strategy.

  • Enable organization-wide adoption of data-driven decision-making.

Working Conditions

  • Hybrid work model with collaboration across global data and engineering teams.

  • High autonomy with clear accountability for outcomes.

  • Regular engagement with senior product and business leaders.

  • Occasional travel for planning sessions, reviews, or cross-team summits.

Job Function

  • Applied Machine Learning & AI

  • Predictive Modeling & Optimization

  • MLOps & Model Lifecycle Management

  • Data-Driven Product & Decision Systems

  • Responsible AI & Model Governance

  • Enterprise Analytics Enablement

Compensation & Benefits

  • Base Salary: $230,000 – $300,000

  • Annual Performance Bonus

  • Long-Term Incentive Plan (Equity / Performance Awards)

  • Comprehensive Medical, Dental, Vision Coverage

  • 401(k) with Competitive Company Match

  • Advanced Training, Conferences & Research Support

  • Wellness, Mental Health & Family Support Benefits

  • Generous Paid Time Off + Company Holidays

Why Join Us

  • Build AI systems that operate at true enterprise scale with real-world impact.

  • Influence how a global organization designs, deploys, and governs AI.

  • Work on complex, high-value problems, not toy datasets or one-off analyses.

  • Collaborate with world-class engineers, product leaders, and data professionals.

  • Join a company that treats AI as a strategic capability, not a buzzword.

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