We are currently seeking an experienced professional to lead our software engineering team and drive practical improvement initiatives to address SDLC bottlenecks, inefficiencies, and friction points across teams.

Key responsibilities include:

• Partnering with Engineering, Platform, and Risk and Control stakeholders to improve delivery flow, change quality, stability, resiliency, and operational effectiveness.
• Defining and driving the adoption of DORA, SPACE, and broader engineering metrics to create visibility, support prioritisation, and improve performance outcomes.
• Establishing and maintaining automated reporting to provide clear views of current performance, root-cause analysis, trends, and recommended actions.
• Leading engineering and operational automation initiatives across areas such as testing, deployment, patching, recovery, and health checks.
• Creating and maintaining a central engineering knowledge space and operating cadence to support governance, transparency, and continuous improvement.

To be successful, you will have 12+ years of engineering experience across the full software delivery lifecycle, with strong engineering leadership capability and hands-on experience in coding.

You will also bring proven experience across engineering excellence, DevOps, platform engineering, SRE, or software delivery improvement roles, and demonstrate strong ability to identify SDLC bottlenecks, prioritise improvement opportunities, and convert insight into practical cross-team action.

Additional requirements include:

• Strong understanding of DORA metrics and good knowledge of SPACE or broader engineering productivity and developer experience measures.
• Solid knowledge of software development, testing, release management, incident management, service recovery, and operational resilience practices.
• Experience leading automation initiatives across testing, deployment, patching, recovery, and operational health checks.
• An AI-driven mindset, with the ability to identify practical opportunities to use AI to improve engineering efficiency, analysis, decision-making, and delivery effectiveness.
• Excellent analytical, communication, problem-solving, and delivery leadership skills.

You’ll achieve more when you join HSBC.

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As a key member of our engineering team, you will own and operate Figma's core observability stack, including vendor platforms such as Datadog, ensuring high availability, strong data quality, and effective signal-to-noise across metrics, logs, and traces.

You will define and drive the technical strategy for instrumentation standards, observability libraries, agents, and operators used to monitor internal and external facing services. You will also explore and implement innovative, AI-driven approaches to anomaly detection, root cause analysis, signal correlation, and operational automation.

In addition, you will establish clear frameworks for cost attribution, budgeting, forecasting, and alerting across infrastructure and observability spend, enabling teams to make informed tradeoffs.

You will partner with infrastructure, product engineering, finance, and security teams to improve visibility into system health and cost efficiency at scale.

You will lead initiatives to optimize observability footprint and spend, balancing depth of insight with performance and cost considerations.

You will coach and mentor engineers through career development, performance feedback, and technical leadership, fostering a culture of ownership, collaboration, and high-quality execution.

We are looking for someone with 4+ years of experience leading infrastructure, observability, or platform engineering teams, with a track record of delivering highly reliable production systems.

You should have deep hands-on experience with modern observability platforms (e.g., Datadog, OpenTelemetry) across metrics, logs, and distributed tracing.

You should have a strong understanding of distributed systems, instrumentation best practices, SLO design, and incident response workflows.

Experience driving cost transparency and accountability initiatives, including cost attribution, budgeting, forecasting, and alerting in cloud environments is also required.

Preferred skills include experience designing or evolving company-wide observability standards, shared libraries, and agent/operator-based integrations, background in cost optimization for infrastructure or observability tooling, including vendor negotiations and usage modeling, and experience applying AI or machine learning techniques to anomaly detection, root cause analysis, or operational automation.

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Data Science

Compensation

• $293K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Role

We’re hiring a Data Scientist to support our Marketing Innovation pod, a cross-functional team building the internal tools and agentic systems that fundamentally change how we do marketing and serve customers.

We build product-like systems that:

• Deliver high-touch, consultative experiences to millions of SMB customers through agentic lifecycle and sales experiences

• Adapt messaging, creative, and outreach using real-time behavioral signals

• Power intelligent routing, targeting, and engagement decisions at scale, with minimal human-in-the-loop

In this role, you’ll be embedded with Product and Engineering to ensure these systems drive measurable business outcomes.

What You’ll Do

• Define success metrics for agentic marketing systems (e.g., incremental pipeline generated, conversion lift, rep hours saved), including leading indicators that enable weekly iteration.

• Design measurement and experimentation frameworks for always-on systems across lifecycle automation, creative generation, targeting, and routing — using holdouts, staged rollouts, and quasi-experimental methods when needed.

• Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value.

• Translate behavioral and model-driven signals into decisions: what to scale, where to intervene, and how to allocate human and compute attention across segments.

• Build repeatable decision loops (pre-launch criteria → post-launch read → next action) that convert analysis into shipped changes.

What We’re Looking For

• 10+ years in a quantitative role (e.g., Data Science, Decision Science), ideally at a product-led company supporting B2B growth, with exposure to SMB or scaled self-serve motions.

• Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments.

• Strong technical fluency in SQL and Python, including working directly with messy, incomplete behavioral data to quantify impact.

• Proven track record of translating results into shipped decisions (product, lifecycle, targeting, routing).

• Strong business judgment and a bias toward action: able to scope ambiguous problems, define success, and move quickly from insight to strategy.

• Excellent communicator and partner to PMs/Engineers; comfortable influencing stakeholders and presenting recommendations to senior leadership.

Nice to Have

• Familiarity with large language models and AI-assisted operations platforms

• Experience working on operational automation and decision systems (routing, prioritization, optimization)

• Experience operating in early-stage or rapidly evolving environments, including building measurement and experimentation frameworks from scratch.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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