Its not just any job. Its basically the Mr. Darcy of data gigs rare, complex, slightly intimidating, and requires one to look both clever and calm whilst explaining credit risk models (possibly recreating the FinTech industry as you know it). Apparently, it goes something like this You, the Ideal Candidate, Must Have: A Bachelors degree (in something terrifying like Stats, Data Science, Maths, Economics, or “a related field” And no ‘years of experience Googling solutions’ does not counts!) Minimum 5 years in a Fintech data science role (read: you’ve survived start-up chaos, regulatory hoops, and possibly a co-worker named Brett who insists on using the word ‘synergy’ unironically) Fluency in Python, SQL, and machine learning things (scikit-learn, XGBoost, LightGBM basically alphabet soup of data wizardry) Solid grasp of credit risk modeling, scorecards, and being able to explain it without everyone falling asleep Communication skills strong enough to survive meetings where everyones mic is off and no one responds Experience with “alternative data sources” (translation: you once used TikTok data to predict consumer behaviour, and it worked emphasis on ONCE) Bonus if youve worked in BNPL, fintech lending, or fraud analytics (basically: anything that sounds like a subplot in a Netflix thriller) Oh, and if you know your way around Google Cloud chefs kiss Feeling personally attacked by how well this job knows what it wants? Then, by all means, put on your best blazer, channel your inner tech goddess, and apply immediately before someone younger, with more ironic tote bags and a degree in Machine Learning from somewhere Scandinavian, gets there first. Contact Chelsea Fisher on

Your Experiments Will Include: Extracting and refining data from multiple sourcesno stone unturned. Conducting statistical analysis that reveals hidden truths. Visualizing insights that influence credit policy and client segmentation. Collaborating with stakeholders to solve real-world puzzles. Staying ahead of the curve with bleeding-edge tools and techniques. Your Ingredients: 3 years of experience in data science or analytics. Proficiency in GCP , SQL , Python , and Google Looker . Strong statistical and modeling skills. Excellent communicationbecause even mad scientists need to explain their genius. Bonus: experience in financial services or credit analytics. If youre ready to unleash data-driven disruptionstep into the lab.