Job Description
At Mindera, we believe that software is built by people, for people, with high-performance systems that impact users worldwide. We are looking for a Lead Data Scientist to join an agile, collaborative team where your voice matters as much as your code.
n This role is therefore not about inventing a new strategy from scratch. It is about taking an agreed plan, implementing it well, and owning the recommendation models end to end in production.
n
We value empathy, self-organization, and a positive attitude. If you are approachable, communicate clearly, and believe that team fit is just as significant as technical expertise, you'll feel right at home here.
n
At Mindera we encourage the use of AI to assist with coding and related tasks. We find a persons skill in engineering and software craft, has a big impact long term successful delivery, with or without AI. Our goal in the interview process is to understand the candidates knowledge and skill with engineering.
n
If you need to, or plan to use AI, please be transparent with us when you're using it, to avoid issues and misunderstandings that can either: impact your chance of securing the role or impact your success at Mindera.
n
National and international expected traveling time varies according to project/client and organizational needs: 0%-15% estimated.
n
How You'll Contribute:
n You will work closely with the Data Science leadership, engineering teams, becoming the senior hands‐on Data Scientist responsible for progressing the recommendation capability in a disciplined and maintainable way.
n
n
Lead the consolidation of existing recommendation algorithms into a simpler, more coherent recommendation capability.
n
Implement the client's existing technical direction and agreed delivery plan.
n
Review current recommendation approaches and rationalise duplication, inconsistency and unnecessary complexity.
n
Develop, improve and productionise recommendation models across agreed customer and commercial use cases.
n
Own the full recommendation model lifecycle:
n
n
Data and feature development;
n
Model development;
n
Evaluation;
n
Production implementation;
n
Monitoring;
n
Retraining;
n
Ongoing optimisation.
n
n
Work directly in Databricks, using Python and PySpark to develop scalable production workloads.
n
Refactor exploratory or notebook-based Data Science code into maintainable production implementations.
n
Apply solid object-oriented design and software engineering principles to the recommendation codebase.
n
Create reusable and testable components for areas such as candidate generation, scoring, ranking, feature generation and evaluation.
n
Build appropriate automated testing around Data Science and ML code.
n




Work with engineering and platform teams on production integration without handing off ownership of the models themselves.
n
Define and maintain clear offline evaluation frameworks.
n
Support online experimentation and measurement of recommendation effectiveness.
n
Monitor production behaviour and take ownership of model quality once live.
n
Provide clear technical communication to both client stakeholders and the wider delivery team.
n
n
This is a Data Science role with a much stronger production engineering expectation than a typical modelling-only position.
n
We are looking for someone with strong experience in:
n
n
Recommendation systems, ranking or personalisation.
n
Python in production Data Science environments.
n
Databricks.
n
PySpark / Spark.
n
Large-scale customer, product or behavioural datasets.
n
Object-oriented programming.
n
Clean code and software design principles.
n
Modular, reusable and testable ML code.
n
Unit testing.
n
Git-based development.
n
CI/CD practices for Data Science or ML workloads.
n
Model evaluation and experimentation.
n
Deploying models into production.
n
Monitoring and maintaining production models.
n
n
Relevant recommendation experience could include:
n
n
Collaborative filtering;
n
Content-based recommendation;
n
Hybrid approaches;
n
Candidate generation;
n
Ranking;
n
Learning to rank;
n
Embeddings;
n
Representation learning;
n
Personalisation;
n
Experimentation and incremental impact measurement.
n
n
Experience with MLflow, Delta Lake, Databricks Workflows and model lifecycle management would be highly valuable.
n The strategic direction already exists.
n
The successful person needs to be comfortable coming into an established environment, understanding the current state quickly, and executing and improving the agreed approach.
n
You will likely be someone who has spent several years as a strong hands‐on Data Scientist and has gradually taken on more responsibility for how your models are engineered, deployed and operated.
n
You should be equally comfortable:
n
n
Discussing recommendation methodology;
n
Reviewing model performance;
n
Writing Python;
n
Refactoring code;
n
Designing clean class structures;
n
Working in Databricks;
n
Debugging PySpark;
n




Writing tests;
n
Working through production issues;
n
Explaining technical trade-offs to stakeholders.
n
n
You will need to be pragmatic.
n
We don't need someone who immediately wants to redesign everything. They need someone who can understand the existing plan, challenge it where necessary, and then drive it through to a high-quality implementation.
n
You will also be expected to:
n
n
Build credibility quickly with senior client Data Science stakeholders;
n
Operate independently within the client team;
n
Communicate progress, risks and technical decisions clearly;
n
Work collaboratively rather than positioning yourself as an external reviewer;
n
Balance client delivery priorities with good engineering practice;
n
Leave the recommendation capability in a stronger and more maintainable state than you found it.
n
n
THE PERKS OF BECOMING A MINDER:
n
n
Private medical subscription
n
Private medical subscription for children
n
Counseling and psychotherapy services
n
Reimbursement for eyeglasses
n
Self-proposal salary process
n
Annual profit distribution, subject to company performance and board decision
n
Mindera Unit Plan
n
Flexible benefits options (sports, medical, cultural, donations)
n
Trainings and learning opportunities to grow within your role
n
Coaching and development guidance
n
25 days holiday
n
Flexibility to choose where you work from
n
Vacation incentive
n
Parties, gatherings & trips
n
n
WHY MINDERA?
n
We're thrilled to have the opportunity to share with you what it's like to be a part of the Mindera community.
n
We're a group of friendly and talented individuals who work together to bring projects to life, in a fun, politics-free environment.
n
Our culture is all about being adaptable and self-organized. We want Minders to take risks, make decisions, work together and feel free to be their most authentic selves, every single day.
n
We work closely with our clients to truly understand their products and develop high-performance, resilient, and scalable software systems that make a real impact for their users and businesses, worldwide.
n
We are proud of our work, we're always learning and growing in our Agile and collaborative environment, and we hope you'll love it here just as much as we do!
n
Check out our Handbook & get to know us better:

n
Mindera around the world: Porto, Portugal | Aveiro, Portugal | Coimbra, Portugal | Leicester, UK | San Diego, USA | San Francisco, USA | Chennai, India | Bengaluru, India | Blumenau, Brazil | Cluj-Napoca, Romania | Valencia, Spain | Casablanca, Morocco | Melbourne, Victoria, Australia & Remote

📌 Lead Data Scientist (Victoria)
🏢 Mindera
📍 Victoria