Principal Product ManagerData & ML Platforms
I build and scale machine learning products. 15+ years across e-commerce, adtech, and fintech — search, recommendations, customer data platforms. Currently founder at Irrational Signals.
Numbers do the work.
- +20%search customer satisfaction
Multi-intent ranking framework. Takealot.
- 3×recommendation impression share
Flexible context-aware API. Takealot.
- Petabytecustomer data platform
Composable CDP, multi-brand, identity resolution. Takealot Group.
- 100+/hrtrading signals
Irrational Signals. US equities · 3 sectors · 6 batches per market day.
- 80% → 30%GeoIP-to-WiFi conversion target
Micro Networks model. Vicinity Media (proof-of-concept).
- 15+years
E-commerce, adtech, fintech. MBA. Cape Town.
A project execution framework for machine learning.
Built at Takealot Group to make the gap between exploration and productionisation explicit. The diamond below is the core visual: ideation expands into multiple candidate paths, then contracts into a single productionised model. Click any stage for detail.
The month markers are illustrative. Actual cadence depends on stack maturity and dataset readiness — modern tooling (synthetic data generation, LLM-assisted data prep, foundation-model baselines) compresses several phases substantially.
Running the 6-month framework in practice surfaced two missing phases. The longer 15-month variant adds these as inline additions to the same diamond — not new structure, just acknowledgement that real ML work sometimes needs a dataset detour and a thinking pause.
- Learning 01Inserted before Generate Training Data
Dataset Exploration Phase
Sometimes the bottleneck isn't the model — it's the dataset. Before committing to training, generate multiple candidate datasets in parallel and A/B test which one produces the most useful signal. This adds a ~3-month phase up front (Generate Dataset → Candidate Dataset 1 / n → Serve Dataset as A/B Test) before model exploration starts.
- Learning 02Inserted after A/B Test, before retraining
Investigation Buffer
The A/B test doesn't always yield a clear answer. Reserve dedicated time after a test to investigate the result — understand the customer behaviour underneath the metric — instead of forcing immediate iteration. This stops cascading dependencies when an A/B test result requires deeper analysis (e.g. the Category Intent rollout, which needed investigation before promotion).
Both additions extend the project shape — a dataset arc up front, a thinking pause at the end. In practice they can add real time to a programme, though modern tooling (synthetic data generation, LLM-assisted analysis) compresses both phases substantially.
The diamond is calibrated for classical supervised machine learning — train a model against labelled data, evaluate offline, A/B test, productionise. The shape of the work still holds for agentic workflows: same expansion / contraction logic, same need for explicit checkpoints. Several stages just compress or change form.
- Generate training dataPrompt design · RAG corpus · synthetic data
- Candidate Model nCandidate prompts · tool sets · orchestration patterns
- Iterative trainingPrompt iteration · retrieval tuning · tool-spec refinement
- Offline POC evaluationLLM-as-judge · behavioural tests · red-team probes
- A/B test or internal testA/B with completion rate · human eval · latency budgets
- Productionise + stageGuardrails · rate limits · fallback prompts
- Drift monitoring+ hallucination rate · tool-call accuracy · latency
The two learnings (the dataset detour and the investigation buffer) carry over directly. The investigation buffer matters more in agentic work, not less — behavioural failures rarely have a clean numeric answer.
Career
- 2025 — present
Founder & Principal Product Manager
Irrational SignalsAPI-first ML-driven trading signals platform. Live subscription product with automated billing. Python SDK + REST API.
FintechMLAPI products - 2024 — 2025
Group Product Lead — Customer Data & ML Platforms
Takealot GroupEnd-to-end product strategy for the Customer Data Platform, CRM, fraud, and insights across the multi-brand portfolio. Architected petabyte-scale data platform; implemented data governance frameworks; scalable analytics architecture.
Customer Data PlatformData governanceMulti-brand - 2022 — 2024
Group Product Lead — AI/ML (Search & Recommendations)
Takealot GroupMachine-learning product strategy for search and recommendations across platforms. Multi-intent search framework (+20% customer satisfaction). Flexible recommendations API (3× impression share).
SearchRecommendationsML platforms - 2020 — 2022
Product Manager — AI/ML (Search & Recommendations)
Takealot.comDelivered the machine-learning product roadmap for core search and recommendation performance. Defined and tracked North Star metrics. Productionised models with data scientists and ML engineers.
SearchRecommendations - 2017 — 2020
Product Lead — AdTech Platform
Vicinity MediaOwned the advertising-tech platform strategy: audience data, campaign systems, attribution. Built the data science function. Out-of-home (OOH) attribution linking offline ad exposure to in-store behaviour. WiFi-clustering and point-of-interest visit models.
AdtechAttributionAudience - 2008 — 2015
Consulting Manager
Cape Value GroupData analysis and market modeling across financial services and public sector. Business strategy, forecasting, and operational planning.
ConsultingModeling
Irrational Signals
- Entry
- $87.45
- Exit
- $88.52
- Sector
- Tech / Semis
- Expected return
- +1.23%
Every signal includes direction, win rate, entry / exit targets, and sector. Pro Max adds a horizon-end timestamp and live preflight checks (price drift, intraday range, relative volume).
Selected work, in depth.
- Case 012020 — 2024
A multi-intent modelling framework for search
Reframing ranking from a single black-box model into composable intent models — the FY23–FY25 vision.
SearchML Platforms3-Year VisionTakealot GroupRead → - Case 022022 — 2024
Flexible recommendations: a context-aware API
One API serving every surface — PDP, cart, wishlist, search, home. Context-aware delivery from a composable model library.
RecommendationsPlatform ArchitectureAPI DesignTakealot GroupRead → - Case 032024 — 2025
Composable customer data platform
Petabyte-scale, first-party tracking events, cross-device and cross-brand identity resolution. Multi-brand activation.
Customer Data PlatformData PlatformIdentity ResolutionTakealot GroupRead → - Case 042017 — 2020
Micro Networks: a proof-of-concept for clawing back precise location
A POC model designed to extend Vicinity's existing location accuracy into the 80% of ad requests where users didn't share their coordinates.
AdTechLocation accuracyProof of conceptVicinity MediaRead → - Case 052025 — present
Irrational Signals: intraday trading signals as an API
Statistical signals on US equities, delivered via REST API and Python SDK. Free tier through Pro Max — live subscriptions on Stripe.
FintechAPI-firstPython SDKFounderRead →