Within AFE, the Portfolio and Climate Risk (PCR) modelling team plays a central role in delivering core portfolio risk analytics alongside climate, energy, and transition risk modelling capabilities used across BlackRock's investment platform.

The Portfolio & Climate Risk (PCR) modelling team is responsible for the research, development, and continuous evolution of BlackRock's multi-asset portfolio risk models and climate- and energy-related risk analytics delivered through the Aladdin platform.

The team leads the development of foundational portfolio risk analytics, including factor-based risk models, Value-at-Risk (VaR), tracking error, and stress testing across equities, fixed income, and derivatives. These analytics support portfolio construction, risk oversight, regulatory reporting, and client decision-making, providing a consistent and scalable risk language across investment workflows.

In parallel, the team develops financially grounded climate, energy, and infrastructure risk models, including climate physical risk, energy systems, energy transition, decarbonization pathways, and geospatial analytics. These models are integrated into asset valuation, portfolio monitoring, due diligence, and client reporting, translating complex climate and energy signals into decision-relevant insights embedded across Aladdin platforms.

As a Vice President in the Portfolio and Climate Risk Modeling team, you will provide technical leadership across power market and energy transition modeling while operating within a broader portfolio risk modeling ecosystem. The role combines hands-on quantitative research with strategic ownership of models, stakeholder engagement, and governance. You will help shape modeling roadmaps, mentor junior modelers, and ensure analytics meet the standards required for enterprise-scale investment use.

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You will set technical direction, mentor a high-caliber science cohort, and partner closely with engineering, PM, UXR, and policy to ship end-to-end outcomes. You will contribute to the development of the next generation of MSN that is adopting the latest generative AI techniques.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day, we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

• Lead content-quality understanding at scale.
• Design and deploy models that assess credibility, usefulness, freshness, safety, and diversity across modalities; reduce misinformation/toxicity error rates through prompt- and model-level innovations; build human-in-the-loop and active-learning pipelines that get better over time.
• Champion safety & trust. Partner with policy and platform teams to encode safety standards and editorial principles into the ML system; create red-teaming, adversarial, and safeguard layers for generative and curated experiences.
• Scale E2E ML systems. Collaborate with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback; drive architectural investments that increase agility and reliability of Discover's AI platform.
• Own evaluation and experimentation. Define offline metrics (e.g., Rejection Rate, ERR, Defect Rate) and online methodologies (A/B tests, interleaving, counterfactual & bandit approaches) to confidently attribute business impact and guard against regressions.
• Mentor & influence. Provide technical leadership across problem framing, methodology selection, code quality, and publishing/knowledge-sharing; uplevel peers through design reviews, deep-dives, and principled decision-making.
• Stay close to users. Translate user engagements and behavioral history into model objectives and product bets; ensure our AI solutions elevate relevance, transparency, and engagement for real users.

Qualifications:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field & related experience (e.g., statistics predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• Experience working with natural language understanding.
• Experience in Python and at least one major deep learning framework (PyTorch/TensorFlow) with large-scale data processing and training.
• Experience with evaluation & experimentation (offline metrics, A/B testing, bandits) and ML model development lifecycle.
• Preferred Qualifications: Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• Have publications at top AI/ML conferences (e.g., KDD, SIGIR, EMNLP, NIPS, ICML, ICLR, RecSys, ACL, CIKM, CVPR, ICCV, etc.).
• Expertise with LLMs (prompting, RAG, Parameter-Efficient Fine-Tuning), multimodal modeling, and retrieval-augmented recommendation; familiarity with counterfactual learning and multi-objective optimization.
• Experience building content integrity/safety systems (e.g., misinformation, harmful content, low-quality/duplicate detection) and quality-aware ranking.
• Demonstrated ability to lead cross-disciplinary efforts (PM, ENG, UXR, editorial/policy) from idea to shipped business impact; mentoring scientists and setting technical vision.
• Familiarity with Microsoft stack (e.g., Azure ML, Kusto, Synapse, Azure AI Foundry).

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

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Key responsibilities:

• Orchestrate the Innovation Flywheel: Manage the lifecycle of agentic innovations from early research bets to incubation and upstreaming into the unified Gemini recipe.

• Lead complex engineering workstreams focused on core agentic capabilities.

• Drive the development and adoption of eval benchmarks to ensure model improvements are rigorously validated on benchmarks like SWE-bench and GDPVal.

• Cross-Functional Integration: Build resilient pathways between DeepMind technical experts and partner product teams, ensuring a 'multi-way win' where a single agentic advancement (e.g., skill writing) unlocks value across multiple Google surfaces.

To succeed in this role, you will need 8+ years of experience leading complex, organizational-level technical programs in a software development or R&D environment. You should have a deep understanding of the AI model development lifecycle, including pre-training, post-training, and infrastructure constraints, especially in agentic development. You will also need proven ability to manage engineering backlogs, navigate technical ambiguity, and drive execution on high-priority research strikes.

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