Data Scientist(Malvern)

As a Data Scientist at Tiger Analytics, you will be responsible for collaborating with business partners to develop innovative solutions, communicating your analytical approach, and leading the integration of analytic methods into various applications. You'll also create scalable models, engineer features from diverse datasets, and coach a community of Data Scientists. This role emphasizes the value of data-driven decision-making and encourages contributions to long-term processes and standards.

To qualify for the Data Scientist position at Tiger Analytics, candidates should have a Bachelor's Degree in Data Science, Computer Science, or a related field, along with a minimum of 5 years of experience in Data Science and Machine Learning. Proficiency in R and R Shiny, experience with machine learning techniques, and a solid understanding of data visualization tools like Tableau are essential for success in this role.

Essential skills for a Senior Data Scientist at Tiger Analytics include proficiency in machine learning concepts and techniques, strong analytical problem-solving skills, and the ability to deliver solutions using various analytical models like NLP and neural networks. Additionally, exceptional communication skills for collaborating with business partners and prioritizing tasks effectively are crucial for this role.

Tiger Analytics fosters a culture of continuous learning and development for its Data Scientists through collaboration, coaching, and engagement with the broader analytics community. You will have the chance to stay updated with industry trends via conferences and community events, allowing you to enhance your skills and share your passion for data science.

The work environment at Tiger Analytics is fast-paced and entrepreneurial, providing significant opportunities for individual responsibility and career development. You'll work within a collaborative team of experienced professionals, fostering an innovative atmosphere that encourages sharing ideas and utilizing cutting-edge techniques in advanced analytics.

In approaching this question, discuss specific machine learning techniques you've implemented, such as classification or regression. Mention various projects where you applied these techniques, the results achieved, and how you overcame challenges along the way. Providing tangible examples demonstrates your depth of knowledge and practical application.

Explain your process for validating and reconciling conflicting data sources. Discuss how you assess data quality, utilize domain knowledge to contextualize the data, and potentially collaborate with stakeholders to clarify discrepancies. Highlighting your analytical and problem-solving skills showcases your ability to manage complexity.

To effectively answer this, describe your approach to prioritization, including assessing project urgency, business impact, and resource availability. Emphasize your organizational skills, the tools you use for task management, and provide an example of how prioritization led to successful project outcomes.

Use this question to provide a detailed account of a specific analytical model you developed. Discuss its purpose, the methodologies used, and the outcomes it facilitated for the business. Highlight both technical aspects and how clear communication of results to stakeholders influenced decision-making.

Describe how you prioritize collaboration in your projects. Share examples of how working with cross-functional teams has led to innovative solutions, ensuring you highlight your communication skills and ability to integrate feedback. Show that you value team dynamics in achieving results.

Discuss your commitment to continuous learning through attending conferences, participating in online courses, or reading industry publications. Mention specific resources you find valuable and how you integrate new knowledge into your work. This demonstrates your proactive approach to professional development.

Explain your approach to making complex models interpretable, like using visualization tools or simplified explanations to convey your findings. Share examples of how you've successfully communicated model outputs to non-technical teams, helping them appreciate the insights derived from your work.

In your response, emphasize the benefits of data-driven decision-making, such as increased accuracy, informed strategic direction, and the potential for recognizing trends before they emerge. Share a real-world example where data-led decisions contributed to business success, showcasing your advisory skills.

Identify the programming languages and tools you use regularly. Discuss how your proficiency in languages like R or Python, along with familiarity with tools like Tableau and R Shiny, enables you to execute projects efficiently. Mention how these tools integrate into your workflow and enhance your outputs.

Outline your criteria for measuring a model's success, such as accuracy, precision, recall, or ROI. Provide examples of how you monitor performance and adjust models based on feedback, showcasing your understanding of model evaluation throughout its lifecycle and commitment to continuous improvement.