Enabling safe and rewarding digital lives for genuine people, everywhere

We make it our mission to ensure more genuine people have digital access to opportunities, and businesses have access to more genuine people. Our technology draws on diverse and reliable data to create a single point of truth for identity and address verification.

With over 30 years of experience behind us our team and technology are focused on enabling safe and rewarding digital lives for everyone. Regardless of age, location or background, genuine people everywhere should be able to digitally prove who they are and where they live.

The Role

We're hiring a Technical Product Manager in Kuala Lumpur to shape Loqate's next-gen addressing, verification and geospatial products. You'll lead customer discovery, partner with engineering to ship AI-assisted validation capabilities, and drive measurable outcomes (conversion, accuracy, and latency) for developers and product teams worldwide. Experience in verification/data-quality/ecommerce/logistics preferred. Apply to join an empowered team building at global scale.

Why this role matters

Loqate powers some of the world's highest-volume, most business-critical verification workflows from global ecommerce checkouts to enterprise onboarding, fraud prevention, logistics, and payments. At the heart of this sits our verification engine: the systems that parse, normalise, match, score, enrich, and validate location and contact data in milliseconds, at global scale, with "enterprise grade" reliability.

We're looking for a technical product manager who thrives in this space, someone who can sit comfortably between customers, engineers, and professional services who deeply understands how verification engines work, and can drive meaningful improvements in accuracy, latency, throughput, and resilience.

This role is about building the engine, not just the experience.

The opportunity

You will own the core verification engine for Loqate's location products including address capture, validation, parsing, normalisation, geocoding, and confidence scoring with a mandate to:

Improve performance, accuracy, and scale

Evolve platform and engine capabilities

Support enterprise customers with complex technical needs

Productise AI and ML innovations safely and pragmatically

You'll work as part of an empowered product trio (PM, Engineering Manager, Tech Lead), but this role demands real technical depth: you will actively participate in architectural discussions, trade-off decisions, and engine optimisation work.

Your remit will include identification, sizing and delivery of the most valuable customer problems, shape differentiated solutions with engineering and data science, and ship measurable outcomes that improve conversion, deliverability, and trust.

This is a hands-on product role: discovery with customers, shaping product strategy, collaborating deeply with engineering, and driving execution. You'll connect the dots across platform capabilities, ML/AI models, SDKs, APIs, tools, and docs so developers and product teams can build with confidence.

What you'll do

1) Own the verification engine (not just features) Be accountable for the core verification pipeline: input, parsing, normalisation, matching, scoring, output. Understand and evolve the rules-based and ML-driven components, including fallback strategies and confidence thresholds. Drive improvements in:

Accuracy and coverage by country and data source

Precision/recall trade-offs

Determinism, explainability, and auditability for enterprise use cases

2) Act as a technical product partner to engineering

Engage deeply with engineering on system design and performance, including:

Response times (p95/p99 latency)

Throughput and concurrency

Caching strategies and data locality

Model evaluation and runtime costs

Reliability, resilience, and bulk processing

Deployment flexibility and ease of set up

Help frame engineering decisions in customer and business terms, balancing performance, cost, and scalability. Translate complex technical constraints into clear product choices and trade-offs.

3) Lead "enterprise-level" customer engagement

Act as a credible technical counterpart for enterprise customers and partners. Participate in technical discussions covering:

API behaviour and configuration

Latency and performance expectations, including cache settings

Verification logic and edge cases, showing our competitive differentiation

Data quality, confidence scoring, and explainability

Bring real customer problems back into discovery, prioritisation, and engine evolution.

4) Optimise and evolve the product engine

Apply prior experience optimising a product engine to improve speed, quality, cost, and scalability over time. Establish engine health metrics and dashboards, including:

Latency distributions per dataset/country/operating mode.

Use Product Ops insights to assess accuracy and product quality

Failure modes (assessed from support cases and customer insights)

Cost per transaction/deployment

Run controlled experiments and rollouts to validate engine improvements before general availability releases.

5) Productise AI and data science responsibly

Partner with Product Ops to turn ML capabilities into production-ready engine components, including address parsing and normalisation models and fuzzy matching and entity resolution. Ensure strong evaluation practices to include offline benchmarking vs baselines. Balance innovation with enterprise trust and predictability.

6) Drive outcomes, not output

Define success in terms of measurable improvements, not shipped features:

Higher first-time match rates

Faster response times

Better customer downstream conversion and deliverability (customer value)

Reduced customer friction and support load for internal teams

Maintain a clear, evidence-based roadmap focused on engine capabilities and leverage.

What success looks like in 30-60-90 days?

30: You've spoken with 12-15 customers/partners; mapped top problems and potential goals.

60: Opportunity solution trees in place; 2-3 problem bets validated with prototypes or data; OKRs and goal(s) agreed.

90: First thin-slice in production improving a key metric and/or business outcome!

What you'll bring (person specification)

Product management experience in one or more of: address intelligence, contact verification, fraud/risk, ecommerce checkout, logistics/last-mile, geospatial, or enterprise data quality (APIs/platforms).

Deep customer empathy and a track record of continuous discovery (interviews, usability, prototyping, hypothesis testing).

Proven experience as a Technical Product Manager or Product Manager working very close to core systems or engines.

Strong understanding of API-driven, high-throughput, low-latency systems.

Demonstrated ability to lead engineering teams through outcomes, not feature mandates and excellent prioritisation and sequencing instincts and methodologies.

Experience working with enterprise customers and managing complex technical requirements.

Platform mindset: API and SDK lifecycle, versioning and deprecation, self-serve docs/samples, observability, SLAs.

Familiarity with AI/ML in production: model evaluation, offline/online testing, data governance, and responsible AI considerations.

Effective stakeholder management: aligning Sales, Customer facing teams, Marketing, Legal/Compliance, and regional teams.

Excellent communication in English; comfortable working across UK/EU/US time zones.

Bonus: Experience with postal/address standards, mapping vendors, or payments/risk ecosystems.

How we work

Empowered product teams: PM + Design + Engineering own outcomes, not task lists. Dual-track discovery & delivery: We de-risk value, usability, feasibility, and viability before we commit. Thin slices, shipped often: Maximise learning velocity and minimise cycle time. Clear strategy & measurable bets: OKRs and decision docs over lengthy PRDs. Customer in the room: Frequent exposure hours with real users and developers.

Practical details

Location: Kuala Lumpur, Malaysia (hybrid). Some travel in APAC and occasional trips to the UK/US for team collaboration and customer discovery.

Reporting line: Director of Product (Loqate, GBG).

Collaboration: Engineering, Data Science, UX, Technical Writing, Partner/Alliances, Sales, CS, Support, Legal/Compliance.

Why join GBG?

Mission-critical products used by leading global brands at massive scale.

A platform mandate: build once, power many experiences.

A chance to shape the frontier of verification and geospatial AI with real-world, measurable impact on trust, conversion, and delivery.

A culture that values curiosity and craftsmanship and gives you the autonomy to do the best work of your career!

To find out more

As an equal opportunity employer, we are dedicated to creating a diverse and inclusive workplace where everyone feels valued and empowered. Please inform your GBG Talent Attraction Partner if you require any reasonable adjustments to the interview process.

To chat to the Talent Attraction team and find out more about our benefits and why we're a great place to work, drop an email to behired@gbgplc.com and we'll be in touch. You can also find out more about careers at GBG and check out our current opportunities at gbgplc.com/careers.

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