Data Scientist

Why Keyrus, Why Now!

Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.

For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence.

AI does not replace humans. It repositions us to a place no system can follow: understanding, deciding, designing, and creating.

At Keyrus, you will not just develop skills — you will develop judgment. Your expertise sharpens with every system you architect, every client challenge you solve, and every deployment that compounds on the last.

Over time, you grow into one of the rarest professionals of the intelligence era: someone who bridges data, AI, and human decision-making at scale, across industries and geographies. This is not a role you fill. It is a discipline you master and a story you help write to become a Keyrus Architect of Intelligence.

Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

Role Details

📍 Job location: Remote (Portugal or Spain-based)
🕒 Contract type: Employee
🗓 Target start date: ASAP
⏰ Working hours: Full-time
💵 Compensation: €40k to €46k gross per year

📋 What You'll Architect

As a Data Scientist, you will help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision-making.

This role is both technical and consultative, perfect for someone who can combine hands-on data science, advanced analytics, machine learning, and stakeholder collaboration to deliver impactful business solutions. We’re looking for professionals who think like architects and act like builders.

Responsibilities

• Collect, process, and analyse structured and unstructured datasets using Python, SQL, and modern data science techniques to generate actionable business insights.

• Develop, validate, and deploy machine learning models and statistical models to support forecasting, customer analytics, and predictive business use cases.

• Perform feature engineering, data preparation, and model optimisation to improve model accuracy and business impact.

• Design and maintain analytical workflows and data pipelines, collaborating with Data Engineering teams when required.

• Identify trends, patterns, risks, and opportunities through the analysis of complex datasets.

• Translate analytical findings into actionable recommendations and present results to both technical and non-technical stakeholders.

• Collaborate with clients, business stakeholders, and cross-functional teams to understand requirements and deliver data-driven solutions.

• Support the development of data science best practices, modelling methodologies, and reusable analytical frameworks.

• Apply predictive analytics, customer analytics, and advanced statistical techniques to solve real-world business challenges.

• Contribute to the successful delivery of client-facing analytics projects from discovery through implementation.

👤 Who You Are

• You are curious, analytical, and motivated by solving meaningful business challenges.

• You enjoy turning complexity into clarity and action.

• You balance technical thinking with business understanding.

• You are comfortable working in collaborative and international environments.

• You take ownership of your work and follow through on commitments.

• You value continuous learning and are motivated by long-term professional growth.

• You communicate clearly and effectively with a variety of stakeholders.

🛠️ What You Bring

Qualifications & Experience

• Relevant academic background in Data Science, Applied Mathematics, Statistics, Computer Science, Engineering, or equivalent professional experience.

• 4+ years of experience in Data Science, Machine Learning, Advanced Analytics, or related data-focused roles.

• Experience delivering analytical solutions from data exploration through to model deployment and business adoption.

• Ability to work effectively in multidisciplinary environments.

• Professional proficiency in English.

Technical & Professional Skills

• Strong hands-on experience with Python and SQL.

• Experience developing and deploying Machine Learning and Statistical Models.

• Solid knowledge of Data Analysis, Data Modelling, and Feature Engineering.

• Experience working with large datasets and extracting actionable business insights.

• Strong understanding of Predictive Analytics, Customer Analytics, and business-driven data science use cases.

• Experience communicating analytical findings to technical and non-technical stakeholders.

• Strong analytical thinking, problem-solving, and critical reasoning skills.

• Familiarity with the full machine learning lifecycle, including data preparation, model training, validation, and deployment.

Nice to Have

• Experience with AWS SageMaker, Google Cloud Platform (GCP), BigQuery, or other cloud-based analytics platforms.

• Knowledge of R, SAS, or SAP Predictive Analytics.

• Experience working with machine learning frameworks, distributed computing environments, or large-scale data processing solutions.

• Experience building time series forecasting models.

• Experience within banking or financial services domains.

• Experience in consulting or client-facing environments.

• Exposure to international projects and multicultural teams.

• Additional certifications in Data Science, Machine Learning, Cloud, or Analytics (e.g., Azure DP-100, AWS Machine Learning, or Google Professional Machine Learning Engineer).

⭐ What Makes You Successful

• You focus on outcomes rather than activity.

• You approach challenges with curiosity and pragmatism.

• You communicate complex concepts in a clear and accessible way.

• You are comfortable navigating ambiguity and finding practical solutions.

• You contribute to collective intelligence by sharing knowledge and supporting others.

• You combine autonomy with collaboration.

• You continuously look for opportunities to improve systems, processes, and results.

🎁 What We Offer at Keyrus Portugal

• Competitive salary aligned with your experience and the data market

• Meal allowance: €10.20/day

• Flexible benefits plan

• Private medical insurance

• 22 days of annual leave, increasing every 3 years (up to 25 days)

• Continuous learning via KLX – Keyrus Learning Experience

• A collaborative, international, and human-centred work environment

💰 How Our Salary Ranges Work

At Keyrus, salary ranges reflect different levels of mastery and impact within the same role — not different job titles.

• Bottom of the range
  You meet the core requirements and will need ramp-up time and support.

• Middle of the range
  You are fully autonomous from Day 1 and deliver consistently.

• Top of the range
  You are a reference for the role, mentor others, and raise the bar for the team.

Final offers are based on experience, autonomy, scope, and market context, and are discussed transparently during the process.

🔒 Responsible AI & Recruitment

At Keyrus, all stages of our recruitment process are conducted and evaluated by human recruiters and interviewers.
To support accuracy and efficiency, AI may occasionally be used internally by our team exclusively for note-taking purposes during interviews. AI is never used to make decisions.

To ensure fairness, authenticity, and the protection of confidential and proprietary information, the use of AI tools by candidates during the recruitment process is strictly prohibited.

Our commitment to responsible AI practices ensures that hiring decisions are based solely on each candidate’s own skills, experience, judgment, and expertise.

⚠️ Any use of AI assistance during the interview process may result in immediate disqualification from the recruitment process.

♿ Equal Opportunity Statement

We are committed to building an inclusive workplace and encourage applications from all backgrounds, regardless of race, ethnicity, gender identity, sexual orientation, age, disability, or any other protected characteristic.