Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 8 and day 60.
At Wise, jobs usually come down by about day 11.
From 11,734 similar postings that came down, and 358 of Wise’s. Coming down is not always a hire.
Staff ML Scientist / Engineer
Tallinn, Estonia
Actively hiring, first seen by us 3 hours ago- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingOur team exists to enable hundreds of specialized risk ML models – their lifecycle, governance and interface with compliance – while removing as much of the maintenance toil from Data Scientist and product teams.
We split the work in two tracks: Labels and Models
You will drive high-priority projects from proof-of-concept to MVP, to service / tooling, estimate and measure impact.
From the employer’s posting
Our team exists to enable hundreds of specialized risk ML models – their lifecycle, governance and interface with compliance – while removing as much of the maintenance toil from Data Scientist and product teams.
We split the work in two tracks: Labels and Models
Science: drive experiments to prove value of new methodologies / algorithms applied to cross-team domains You will drive high-priority projects from proof-of-concept to MVP, to service / tooling, estimate and measure impact. Qualifications
What you’ll bring
All qualificationsCore experience
- Ability to work with Python and Java;
- Ability to build incrementally, think from first principles and prioritize simpler solutions;
- Good communication skills with technical and non-technical audiences, to engage and manage project stakeholders;
Qualification wording
Excellent Software Engineering skills. Ability to work with Python and Java;
Ability to build incrementally, think from first principles and prioritize simpler solutions;
Good communication skills with technical and non-technical audiences, to engage and manage project stakeholders;
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 13, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Wise is hiring
63 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Scientist hiring is steady
- Takes jobs down quickly
- 41% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Java
- Python
- AWS
- dbt
- Docker
- Iceberg
- Kafka
- MLflow
- S3
- SageMaker
- Spark
- Terraform
- PyTorch
- scikit-learn
- XGBoost
Where the posting names them
- Previous experience as Data/ML Scientist/Engineer in large scale product; - Excellent Software Engineering skills. Ability to work with Python and Java; - Strong drive to solve problems for Data Scientists, high agency in complex scenarios;
- Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery - MLOps: Terraform and AWS infra, ML governance for hundreds of models - Data Engineering: distributed processing at terabyte scale
- Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML - AWS (S3, EMR, SageMaker, Lakeformation) - Terraform, Docker, GitHub CI/CD
Some skills that will make you stand out: - Apache Spark, Iceberg, Kafka, dbt, GraphFrames, Ray - Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML
- AWS (S3, EMR, SageMaker, Lakeformation) - Terraform, Docker, GitHub CI/CD - A/B testing. ML explainability
- Apache Spark, Iceberg, Kafka, dbt, GraphFrames, Ray - Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML - AWS (S3, EMR, SageMaker, Lakeformation)
Your next step
Complete your application on jobs.smartrecruiters.com. The employer’s form will show what is required.
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- Uploading your resume fills in the application form for you, so check what it filled before you send.
Already applied? Track this application
Demand for Data Scientist roles
New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
Job Description
We’re looking for a strong technical contributor with a background in machine learning and systems engineering. Bonus point if they owned the roadmap of services/tools used by more than one internal team.
This is a highly autonomous person that identifies low-hanging fruit worth tackling to move us ever closer to the vision they create for the problem-space they own. They must be able to communicate with stakeholders and users, steer short-term deliveries without compromising milestones in the roadmap, set guidelines and standards.
They collaborate with Data Science Leads to align work that would otherwise be siloed. They review experiments and design documents to improve projects from the get-go.
This role is a unique opportunity to scale and advance the impact of Data Science in Servicing tribe – namely Fincrime, KYC and Customer Support squads. What you build will have a direct impact on Wise’s mission and millions of our customers.
About the Role:
Our team exists to enable hundreds of specialized risk ML models – their lifecycle, governance and interface with compliance – while removing as much of the maintenance toil from Data Scientist and product teams.
We split the work in two tracks: Labels and Models
- Labels: central platform for risk labels
- Models: libraries and services to experiment, eval, promote and track models
Historically Wise is a company of very autonomous and distributed teams, which means different implementations exist to prevent and mitigate risk vectors.
We’ve invested a lot into centralizing risk assessments and their inputs (features) and have strong adoption from FinCrime, Verification, Mitigation and Global Products teams.
We’re now expanding this investment to the next critical component in scaling Wise: more and better ML /AI while keeping auditors and regulators happy.
Here’s how you’ll be contributing:
- Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery
- MLOps: Terraform and AWS infra, ML governance for hundreds of models
- Data Engineering: distributed processing at terabyte scale
- Science: drive experiments to prove value of new methodologies / algorithms applied to cross-team domains
You will drive high-priority projects from proof-of-concept to MVP, to service / tooling, estimate and measure impact.
Qualifications
A bit about you:
- Extensive experience with end-to-end distributed data systems, specially ML-centric ones;
- Previous experience as Data/ML Scientist/Engineer in large scale product;
- Excellent Software Engineering skills. Ability to work with Python and Java;
- Strong drive to solve problems for Data Scientists, high agency in complex scenarios;
- Ability to build incrementally, think from first principles and prioritize simpler solutions;
- Good communication skills with technical and non-technical audiences, to engage and manage project stakeholders;
- Strong problem solving skills with the ability to help refine problem statements and propose solutions taking effort-impact-scalability tradeoff into account;
Some skills that will make you stand out:
- Apache Spark, Iceberg, Kafka, dbt, GraphFrames, Ray
- Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML
- AWS (S3, EMR, SageMaker, Lakeformation)
- Terraform, Docker, GitHub CI/CD
- A/B testing. ML explainability
Additional Information
Interested? Find out more:
What do we offer:
Wise's Restricted Stock Units (RSU's).
For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.
We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
If you want to find out more about what it's like to work at Wise visit Wise.Jobs.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Company Description
Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
More about our mission and what we offer.
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Tallinn, Estonia
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.