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Peter Faasse

Machine Learning Expert
Former data science manager for Career Analytics.
As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.

Hung Do

Machine Learning Expert
Skilled data scientist specialized in math-statistics and agile software development.
Hung is a full-stack data scientist with expertise in both math statistics and agile software development. She has processed financial valuations of multi-million dollars for agricultural corporates, analyzed clinical data for major hospitals in Australia, and developed machine learning models for the EuroLeague - Europe’s premier basketball competition. She brings a wealth of international experience to her teams, having worked and studied in Singapore, France, Germany, Switzerland, and Australia. She completed her Ph.D. at UNSW Sydney with a Dean’s Award Nomination for the Top 10% of Theses.

Santiago Olszevicki

Machine Learning Expert
Data analyst at Ministry of Health.
Biochemist and data analyst with 2+ years of experience in data analyisis, data visualization and machine learning. Currently specialized in health related work as an epidemiologist.

Yogini Naik

Machine Learning Expert
Data and applied scientist at Microsoft.
Yogini Naiki is a data scientist with over two years of experience in providing data-driven solutions for products and three years as a developer. She has worked on NLP, data engineering, descriptive analytics, and data visualization. As a data and applied scientist at Microsoft, she has handled multiple data science and analysis projects, including formulating OKRs and creating metrics and insights. Yogini also served as an analyst for Deutsche Bank. Her technical skills include Python, Java, R, Big Data systems (Cosmos + Scope, Hadoop), Pyspark+Databricks, Power BI, machine learning, and basic SQL.

Didi Adisaputro

Machine Learning Expert
With 10+ experience in data analytics, R development, & engineering.
Didi Adisaputro is a data scientist with over a decade of experience in data analytics, R development, data management, engineering, machine learning, big data, and data intelligence. He has profound experience in automation, building R Shiny dashboard, and data science pipeline build. As a climate intelligence section head at Golden Agri-Resources (GAR), he performs exploratory data analysis using R/Python and presents insights to business stakeholders to drive strategic decisions. His technical skills also include Keras and TensorFlow.

Stephanie Mak

Machine Learning Expert
Senior machine learning engineer for EnergyAustralia.
Stephanie Mak is a highly experienced data and machine learning engineer with a strong track record of delivering data/ML projects, and enabling data-driven decision-making that aligns with business goals. She has also attained the DataBricks Certified Machine Learning Associate and DataBricks Certified Data Engineer Associate Certificates. Her latest work EnergyAustralia includes the design and delivery of DataBricks multi-workspace solutions for enterprise data lakehouse, data science laboratory, and ML engineering.

Laura Halacheva

Machine Learning Expert
Data science lead for Teva Pharmaceuticals.
Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.
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Frequently asked questions

Most teams see matched candidates within 48 hours. Our pre-vetted network means we are not starting from scratch when you reach out. If we do not have the right person available, we will tell you upfront rather than scramble.
Practitioners vet practitioners. Your growth marketer candidate gets interviewed by someone who has run growth at similar companies, not a recruiter checking keywords. We combine technical assessments, portfolio reviews, and cultural fit screening before anyone reaches your inbox.
Contract talent works on defined projects with clear deliverables. Embedded talent integrates into your team for ongoing work, attending standups and using your tools. Both can transition to full-time if the fit is right. We help you choose based on scope, timeline, and budget.
If it is not working in the first few weeks, we will find you a replacement at no additional cost. We are not in the business of defending bad matches. Most clients never need this because we invest heavily in fit upfront.
We offer flexible engagement models: hourly for short-term projects, monthly retainers for ongoing work, or placement fees for full-time hires. No hidden fees, no long-term contracts unless you want them. Your account manager will walk through options on your discovery call.
Yes. Someone who thrived at Google with dedicated QA, DevOps, and product managers may flounder when they have to wear all three hats. We look for people who have operated at your stage, pace, and level of ambiguity.
Yes, and not just the flashy demo-building part. Our Agent Operations practice includes people who have run AI agents in production and dealt with the messy reality of keeping them reliable, cost-effective, and safe.
Most marketplaces optimize for volume. They send you 10 candidates hoping one sticks. We optimize for fit. We would rather say “we do not have the right person” than waste your time. Our 95% satisfaction rate comes from being selective, not fast.