Sharegain began with one question: If the largest institutions solely exercise the right to lend their stocks, bonds, and ETFs, what would it take to unlock this revenue opportunity for every investor?
Our team of experts in the UK, US and Israel built the solution: a platform that empowers online brokers, private banks, and wealth managers to offer securities lending to their clients. We call it SLaaS: Securities Lending as a Service. It’s a fully digital, customizable, end-to-end solution that automates front- and back-office operations. Institutions and investors are now free to earn more from what they own.
Every Sharegainer has their own backstory, but we all share an ambition to do things differently – bigger, better, and greater. Together we’re on a mission to democratize capital markets by building a more liquid world. The more we share, the more we all gain.
What you'll do
We're looking for a hands-on, all-round data scientist who thrives on turning complex financial data into real business impact. This isn't a role for someone who stays in notebooks — you'll work across the full data and ML lifecycle, from raw pipelines to production models and back again, contributing meaningfully to how Sharegain understands its markets, clients, and risk.
You'll be embedded in a fast-moving fintech environment, working closely with data, engineering, and business stakeholders to solve problems that matter.
Design, build, and maintain end-to-end data pipelines and ML models on the Databricks platform, from ingestion through to business consumption
Own the full MLOps lifecycle — experiment tracking, model registry, deployment, monitoring, retraining, and deprecation — not just the modelling phase
Develop predictive models and analytics across securities lending, client behaviour, market dynamics, and risk
Collaborate with business teams to translate commercial questions into data problems — and data findings into decisions
Own data quality, lineage, and governance within your domain, working within Unity Catalog and Delta Lake frameworks
Build and maintain CI/CD pipelines for ML workflows, ensuring reproducibility and reliable delivery to production
Contribute to the evolution of Sharegain's data infrastructure and MLOps practices
Requirements
5–8 years of experience in data science or a closely related role, with at least 2 years working hands-on with Databricks and the broader ecosystem (Delta Lake, MLflow, Unity Catalog, Databricks Workflows)
Demonstrated experience owning the full MLOps lifecycle end-to-end: from experiment tracking and model versioning through to deployment, monitoring, drift detection, and retraining pipelines
Track record of taking models from development to production and keeping them healthy over time
Comfortable across the full stack, data engineering, feature engineering, model development, and operationalization, not siloed in one area
Strong communicator who can engage both technical peers and non-technical stakeholders
Proven experience in financial services: securities, capital markets, wealth management, lending, or adjacent domains