We’re looking for a Machine Learning Engineer / Data Scientist to join vyking’s growing data team and help shape the future of intelligent, data-driven player experiences. You’ll work at the intersection of data, AI, and product — developing predictive models, recommendation systems, and LLM-driven features that directly impact player engagement and business growth.

You’ll own projects end-to-end: from data preparation and feature engineering to model training, deployment, and monitoring. If you’re passionate about building scalable ML solutions and pushing the boundaries of applied AI, we’d love to hear from you.

Essential Job Tasks

Design, build, and deploy machine learning models (classification, regression, recommendation).
Develop player prediction models for churn, LTV, and engagement metrics.
Build and enhance recommendation systems using collaborative filtering and hybrid techniques.
Integrate and optimize third-party LLM APIs (e.g., OpenAI, DeepSeek) for smart, AI-powered features.
Work with large-scale datasets to extract insights and engineer impactful features.
Collaborate with data engineers, product managers, and marketers to ensure scalable model pipelines.
Communicate data-driven insights in a clear, actionable way to diverse stakeholders.

What we’re looking for:

3+ years of experience as a Machine Learning Engineer, Data Scientist, or similar role.
Strong skills in Python (pandas, NumPy, scikit-learn) and SQL.
Proven experience with supervised learning and recommendation systems.
Hands-on experience integrating LLMs and third-party APIs.
Solid grasp of the end-to-end ML lifecycle — from concept to production.
Excellent problem-solving, communication, and collaboration skills.

Nice-to-Haves:

Experience with TensorFlow, PyTorch, or Hugging Face.
Knowledge of A/B testing, big data tools (Spark), or cloud ML platforms (AWS, GCP, Azure).
Experience deploying or fine-tuning LLMs in local or hybrid environments.

Preferred Industries:

Previous experience in iGaming, fintech, or large-scale online platforms is a plus

Soft Skills

Analytical Thinking: Ability to approach complex problems logically and translate data into actionable insights.
Curiosity & Innovation: Passion for exploring new algorithms, tools, and approaches in machine learning and data science.
Collaboration: Works effectively with cross-functional teams, including engineers, product managers, and business stakeholders.
Communication: Clearly explains technical concepts and data-driven findings to both technical and non-technical audiences.
Adaptability: Comfortable navigating evolving priorities, emerging technologies, and experimental projects.
Problem-Solving Mindset: Proactively identifies challenges, tests hypotheses, and iterates toward effective solutions.
Attention to Detail: Ensures data accuracy, model reliability, and reproducibility in all workflows.
Critical Thinking: Evaluates model performance and business impact objectively, using data to guide decisions.
Ownership & Accountability: Takes responsibility for the full lifecycle of machine learning projects — from idea to deployment.
Time Management: Balances multiple projects efficiently while maintaining high standards of quality and delivery.
Continuous Learning: Eager to stay updated on the latest advancements in AI, ML frameworks, and data science techniques.
Creative Problem Solving: Thinks outside the box to apply AI in innovative ways that enhance player experiences.