(Senior) Machine Learning Scientist

Who are we looking for

We aren't looking for employees; we are looking for mission-driven pioneers. To thrive at Gogolook, you must meet the following standards:

• Impact-Driven Architects: We require individuals who are motivated by the challenge of creating meaningful products that defend society against global fraud.
• Relentless Learners: We expect you to take full ownership of your growth by actively participating in technical communities and workshops, representing Gogolook's expertise on the global stage.
• Assertive Experts: We value those who bring their own professional opinions to the table, engaging in rigorous discussion to ensure we build only the most exceptional products.
• Radical Collaborators: You must thrive in a culture of extreme transparency, where you are expected to consume company-wide information and actively participate in shaping our collective future.

If you feel you fit the bill, come join us!

About the Intelligent Systems Lab (ISL)

The ISL team provides applied research and development capabilities to help achieve long-term strategic goals for Gogolook. Our typical project lifecycle is 6 months, focusing on a transition from ISL team principles to long-running applied R&D. We collaborate closely with other Gogolook teams to:

• Evaluate product needs for intelligent solutions.
• Propose solutions that use cutting-edge AI.
• Create POCs (Proof of Concepts) to demonstrate viability.

Together, ISL and Engineering teams meet both the short and long-term technology needs of the company.

Role Mission

As a Machine Learning Scientist, you will be a core part of ISL's modeling competency. Your mission is to find out - with evidence - which models, features and data actually make Gogolook's anti-fraud products more precise and reliable, and why. This is a scientific role, not an engineering one: you will form hypotheses, design experiments to test them, and justify every direction and every step on a mathematical and empirical basis. "Let's build it this way because it sounds logical" is not a justification. A model that works but cannot be defended mathematically and statistically is not finished.

Fraud is a hard scientific problem. Labels are scarce and heavily imbalanced, every false positive costs user trust, and scammers adapt to whatever we deploy, so the data drifts under you. We need people who can reason about these conditions mathematically, not just tune around them.

You will own end-to-end ML investigations - from experimental design and statistical analysis to a validated model ready for production - running advanced experimental designs independently and catching pitfalls such as overfitting, data leakage and drift before they reach users. With seniority comes scientific leadership: mentoring others on statistical methods, setting ISL's standards for experimental design and statistical analysis, leading strategic decisions on ML methodology, and communicating the evidence to executives. Your exact level is confirmed on successful completion of the 3-month probation, based on the work you deliver.

Responsibilities

• Hypothesis-Driven Modeling: Frame product questions as testable hypotheses; choose and develop models (supervised, unsupervised, time-series, probabilistic) on mathematical grounds and evidence, not convention or intuition.
• Experimental Design: Design, run and interpret experiments - hypothesis tests, confidence intervals, A/B tests, multi-armed bandit, factorial and stratified designs - to decide what to change and when to stop.
• Rigorous Evaluation & Error Analysis: Choose metrics that reflect real cost (e.g. precision-recall trade-offs under class imbalance), quantify uncertainty, and run error analysis that explains where and why a model fails.
• Validity & Robustness: Detect and guard against overfitting, data leakage, selection bias and model drift; establish how a model will behave as scam patterns shift.
• Mathematical & Empirical Justification: Defend every significant decision from first principles and data - the question, the mathematical reasoning, the design, the assumptions, the evidence and the conclusion - written up so that others can reproduce and challenge it.
• Path to Production: Hand validated models to engineering teams with clear acceptance criteria, and define the monitoring that shows whether the model still holds in production.
• Scientific Leadership: Review peers' experimental designs, mentor on statistical methods, shape the research agenda, and define team-wide standards for reproducibility and rigor.

Qualifications

• Proficiency in English, both spoken and written, sufficient to collaborate, document, and present in an English-working environment.
• Graduate degree (Master's or PhD) in AI, Machine Learning, Deep Learning, or a related or supporting field such as Statistics, Mathematics, Physics or Computer Science with an ML focus.
• 6-10 years of experience in machine learning research or applied ML.
• Strong mathematical foundations: probability and statistics, linear algebra, and optimization, applied fluently to real modeling problems.
• A scientific approach to problems: you form hypotheses, test them, and change course when the evidence says so - and can show examples from your past work.
• Mathematical defense of choices: you justify a model, loss, metric or experimental design by its mathematical properties and assumptions, not because it sounds logical or is what others use.
• Evidence-driven communication: you can explain and justify a modeling direction to both technical and non-technical audiences using mathematics and data, not intuition or authority.
• Independent and self-motivated, able to own an investigation from open question to defensible conclusion.
• Proactive communicator who raises methodological and technical risks early.

Preferred Qualifications

• PhD, or a record of peer-reviewed publications or rigorous technical reports in ML or statistics.
• Experience with Bayesian methods, causal inference, or anomaly detection.
• Experience with adversarial or non-stationary domains such as fraud, abuse, spam or security.
• Exposure to cloud ML platforms (AWS SageMaker, Google Vertex AI), and enough familiarity with CI/CD and Docker to hand models over to production teams smoothly.

Skills & Competencies

• Python and the scientific stack (NumPy, pandas, SciPy, statsmodels); SQL for working directly with large datasets.
• ML frameworks: scikit-learn, XGBoost, and PyTorch or TensorFlow.
• Statistical toolkit: hypothesis testing, confidence intervals, power analysis, A/B and multi-armed bandit testing, and resampling methods.
• Git and reproducible experiment tracking.

How to Apply

If you have published articles or papers on ML or AI, please share them with your application. We will ask you to walk us through them during the interview: the question, the method, and how you justified your conclusions.