Data Scientist – AI and Automation in Customer Success

To apply for the Data Scientist – AI and Automation in Customer Success position at Unit4, you should possess a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field. Additionally, having hands-on experience in data science, particularly in SaaS or enterprise software, is highly beneficial. Familiarity with machine learning, natural language processing, and automation processes is essential, as well as proficiency in programming languages like Python and SQL.

As a Data Scientist at Unit4, you'll enjoy a people-centric work environment that nurtures trust and autonomy. You'll experience a culture that promotes a healthy work-life balance, with policies like uncapped time off and Global Wellbeing Days. You'll be supported by talented colleagues and provided with numerous growth opportunities, all while contributing to a company committed to sustainability and social initiatives.

The Data Scientist – AI and Automation role is pivotal to enhancing customer success at Unit4 by creating automation solutions that reduce manual efforts for Customer Success Managers. By developing predictive models that anticipate customer needs, you’ll enable proactive decision-making and support, ultimately leading to improved customer experiences and increased satisfaction.

Key responsibilities of the Data Scientist – AI and Automation position at Unit4 include developing and deploying machine learning models, optimizing customer workflows for automation, building predictive analytics capabilities, and collaborating with cross-functional teams to ensure AI solutions align with business objectives. You’ll also monitor performance metrics and adjust your models for continuous improvement.

Essential technologies and skills for a Data Scientist role at Unit4 include proficiency in Python, SQL, and experience with machine learning frameworks like TensorFlow or PyTorch. A strong understanding of data integration practices and API connectivity is also important. Furthermore, excellent analytical abilities, problem-solving skills, and the capability to translate business needs into technical solutions are crucial for success in this position.

In the Data Scientist – AI and Automation role at Unit4, regular communication with stakeholders is vital. You will present updates on automation initiatives and impact metrics, ensuring stakeholders are informed about the effectiveness of your models and the value they contribute to the organization. Keeping lines of communication open is essential for alignment with customer outcomes.

Unit4 offers numerous opportunities for professional development for Data Scientists, including mentorship programs, access to innovative projects, and a culture that encourages learning from talented industry peers. Additionally, with the company's commitment to growth, you can expect to continually evolve your skills in the ever-changing fields of AI and data science.

In your response, highlight specific projects where you've built or deployed machine learning models tailored to enhance customer success. Discuss how these models improved efficiency or customer satisfaction, and be prepared to provide metrics or qualitative outcomes to support your claims.

Share a detailed example of a manual process you automated, emphasizing the steps you took from analysis to implementation. Discuss the tools and technologies used, any challenges faced, and the results achieved in terms of time savings or improved accuracy.

Explain your strategies for staying up-to-date, whether it be through online courses, professional networks, conferences, or literature. Including specific resources and how they've influenced your work will demonstrate your commitment to continuous learning.

Describe your problem-solving approach when facing data integration issues. Detail the strategies and tools you use to ensure data consistency across multiple systems, emphasizing your understanding of APIs and integration practices.

Share a story where collaboration with cross-functional teams was key to achieving a goal. Highlight how you navigated different perspectives, aligned on objectives, and successfully contributed your data science expertise to the team's efforts.

Discuss metrics such as precision and recall, model accuracy, F1 score, and other key performance indicators relevant to your work. Explain your rationale for choosing these metrics based on the desired outcomes and how they align with business objectives.

Explain your prioritization method, possibly using frameworks like Eisenhower Matrix or MoSCoW, to manage competing deadlines and ensure your projects align with company goals. Providing examples of your project management approach will add depth to your answer.

Share specific instances where you have implemented predictive analytics in your work. Discuss the methodologies you used, the types of data analyzed, and the impact your predictions had on decision-making and customer engagement.

Discuss your experience with various data visualization tools, such as Tableau or Power BI. Explain how these tools have helped you communicate complex data effectively to stakeholders and drive decision-making in your previous roles.

Detail the techniques you employ to ensure data accuracy, such as validating data sources, performing statistical analysis for data quality checks, and collaborating with teams to resolve discrepancies. Providing examples of past challenges you’ve faced can showcase your attention to detail.