Data Scientist - ML Engineer

As a Data Scientist - ML Engineer at Unit4, your responsibilities will include designing and deploying machine learning models that solve complex business challenges. You will collaborate with cross-functional teams to ensure the integration of responsible ML solutions into our products, maintaining performance and reliability throughout the ML lifecycle. Implementing MLOps best practices will be essential for continuous integration and deployment of our models.

To be considered for the Data Scientist - ML Engineer position at Unit4, you should have a Bachelor’s degree in Computer Science, Mathematics, or a related field. A Master’s degree in Data Science or AI is preferred. Strong experience in machine learning, MLOps, proficiency in Python, and familiarity with ML frameworks and tools such as Azure Machine Learning Studio and Databricks are essential. Furthermore, strong problem-solving skills and a collaborative mindset will help you succeed in this role.

Unit4 fosters a culture built on trust, providing Data Scientists with the autonomy to succeed in their roles. The work environment is fast-paced yet flexible, allowing for a great work-life balance through uncapped time off policies and opportunities for remote work. You will be part of a vibrant team that values collaboration and support, ensuring that everyone can excel while contributing to meaningful projects.

Unit4 is committed to implementing responsible AI principles, ensuring that its machine learning solutions are designed with fairness, accountability, and transparency in mind. As a Data Scientist - ML Engineer, you will play a pivotal role in incorporating these ethical standards into the ML models you develop, ensuring that they meet both user and societal expectations.

At Unit4, Data Scientists have ample opportunities for professional growth and development. You will work alongside talented colleagues and mentors, allowing you to learn, grow, and be inspired within the software industry. With an emphasis on continuous learning, you will also have access to various training programs, certifications, and career advancement initiatives.

During your interview, discuss frameworks like TensorFlow, PyTorch, or Scikit-Learn, showcasing your hands-on experience with them. Elaborate on specific projects where these frameworks played a crucial role, demonstrating how you leveraged their capabilities to solve real-world problems.

Share a detailed description of a specific project, including the problem you aimed to solve, your approach to data preprocessing, model selection, and evaluation metrics. Highlight the impact this project had on the business or users and any lessons learned during the process.

Talk about your approach to diagnosing performance issues, such as analyzing model metrics and retraining strategies. Emphasize the importance of continuous monitoring after deployment and how you would iterate to refine models for improved accuracy and reliability.

Explain MLOps as a set of practices that combine machine learning and DevOps. Emphasize its importance in ensuring scalable, reliable, and efficient machine learning models that can be systematically deployed and maintained in production environments.

Discuss your alignment with responsible AI principles, such as fairness, accountability, and transparency. Provide examples of how you assess bias in your datasets and ensure compliance with ethical guidelines in your model development process.

Mention techniques like Recursive Feature Elimination, Lasso Regression, or domain knowledge to justify feature choices. Provide examples of how effective feature selection has improved model accuracy in your past projects.

Share your familiarity with big data tools such as Hadoop, Spark, or Kafka. Reference specific scenarios where you utilized these technologies to ingest and process large datasets, explaining their contribution to your data science workflows.

Discuss your structured approach to ensuring data quality through thorough validation and cleaning processes. Emphasize the importance of compliance with data protection regulations and how you incorporate these considerations into your data workflows.

Highlight your ability to distill complex data insights into clear, actionable recommendations suitable for non-technical audiences. Share examples of visualization tools you’ve used, such as Tableau or Power BI, and how these have facilitated communication in past projects.

Express your commitment to ongoing learning by mentioning resources like research papers, conferences, webinars, and online courses. Discuss how staying abreast of industry developments allows you to apply cutting-edge methodologies in your work.