We are currently recruiting for a Data Scientist within the multidisciplinary Advertising Product Insights team. This role is focused on supporting the scaling of the future of Spotify Advertising, our self-service advertising platform, Spotify Ads Manager. Our work sits at the intersection of R&D and the Ads business, and we are responsible for bringing the right data and insights to our breadth of stakeholders to understand Spotify Ads Manager performance, and the advertiser experience with Spotify.

Our mission is to enable the product and business teams to meet their objectives through evidence-based decision making and customer focus. As a data scientist in this group, you will use a range of data science tools and capabilities to work closely with product, design, engineering, user research, product marketing, and our business stakeholders on one or more of the following areas:

• Customer-facing product development for advertisers
• Data requirements and management for our ever-expanding platform
• Spotify Advertiser and Spotify Ads Manager growth initiatives
• Experimentation, causal inference, and insights for Spotify Ads Manager optimization
• Defining new metrics, forecasts, and benchmarks for product evolution
• Evolving Ads Manager self-service performance and UX reporting

What You'll Do

• Perform analyses on large sets of data to extract practical insights that will help drive product, platform, and business decisions throughout all stages of the product development lifecycle
• Define data requirements and support the implementation of data specs for product launches
• Support the management and maintenance of the Spotify Ads Manager's data and reporting (including dashboards) ecosystem
• Build lasting solutions to surface critical data and performance metrics
• Design and conduct experiments to drive iterative improvement to both user and advertiser metrics through efficient experimentation
• Communicate data-driven insights and recommendations to key partners
• Develop individually to continue to grow your impact, and help mentor peers and early career colleagues

Who You Are

• You have 3+ years of experience in a similar data science role
• You are interested in supporting the growth of a maturing platform, building the foundation for the future of Spotify Advertising
• Ideally, you have experience working in an analytics role that focuses on improving internal decision making while working with one of the platform teams, product teams, sales teams, or marketing teams
• You have the technical competence to perform more advanced analytics: coding skills for analytics and data extraction (SQL, Python - packages such as pandas and numpy) and data visualization (Tableau, Looker, matplotlib)
• You have experience performing analysis with large datasets
• You have a strong understanding of statistics and machine learning
• You value cross-disciplinary work and understand how to match qualitative research with your quantitative analysis
• You are capable of solving very loosely defined problems
• You are a communicative person that values building strong relationships with colleagues and collaborators and have the ability to explain complex topics in simple terms
• You are curious, exhibit a growth mindset, and are excited by working on a constantly evolving team

Where You'll Be

• We are a distributed workforce enabling our band members to find a work mode that is best for them!
• Where in the world? For this role, it can be anywhere in the North America region in which we have a work location
• Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.
• Working hours? We operate within the Eastern time zone for collaboration.

Additional Information

The United States base range for this position is $107,766 - $153,951, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.

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The goal of this work is to develop a predictive method for evaluating material performance under thermal runaway conditions due to escaping gas and particles at materials used in venting structures. To achieve this, a neural network will be designed, trained, and applied to new materials. The neural network will be trained and validated using existing experimental and simulation data. The generated evaluation data will be compared with results from classical substitute and cell tests to evaluate the performance and reliability of the developed approach. Based on the obtained data, characteristic parameters for evaluating material failure under thermal runaway conditions will be identified and derived.

In the first step, a systematic literature review will be conducted on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context. Based on this, a suitable model for evaluating material performance in the context of thermal runaway will be developed, trained, and implemented. The model will be validated using experimental data to assess its predictive accuracy and robustness. Finally, the applicability of the developed approach and potential opportunities for further development will be critically discussed.

Key tasks:

• Conduct a systematic literature review on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context.

• Design, train, and implement a neural network for predictive evaluation of material performance under thermal runaway conditions.

• Evaluate and analyse experimental and simulation data to validate the model.

• Compare generated evaluation data with results from classical substitute and cell tests.

• Identify and derive characteristic parameters for evaluating material failure under thermal runaway conditions.

• Critically discuss the applicability of the developed approach and potential opportunities for further development.

Requirements:

• Bachelor's or master's degree in computer science, mechanical engineering, electrical engineering, or a related field.

• Experience in machine learning and deep learning.

• Familiarity with Python programming language and relevant libraries.

• Good understanding of thermal runaway phenomena and fire protection materials.

• Excellent communication and teamwork skills.

• Ability to work independently and manage multiple tasks.

Preferred skills:

• Experience with neural networks and deep learning frameworks such as TensorFlow or PyTorch.

• Familiarity with simulation software such as ANSYS or COMSOL.

• Knowledge of thermal analysis and heat transfer.

• Experience with data analysis and visualisation tools such as Matplotlib or Seaborn.

• Familiarity with version control systems such as Git.

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Your responsibilities will include identifying areas where ML can create value for our customers, identifying the right ML framing of product problems, working with teammates and Product and Design stakeholders, conducting exploratory data analysis and research, deeply understanding the problem area, researching and identifying the right algorithms and tools, being pragmatic, but innovating right to the cutting-edge when needed, performing offline evaluation to gather evidence an algorithm will work, working with engineers to bring prototypes to production, planning, measuring & socializing learnings to inform iteration, and partnering deeply with the rest of team, and others, to build excellent ML products.

To be successful in this role, you will need to have broad applied machine learning knowledge, 3-5 years applied ML experience, practical stats knowledge (experiment design, dealing with confounding etc), intermediate programming skills, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g. MSc), and scientific thinking skills. Bonus skills and attributes include track record shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, strong stats or math background, visualization, data skills, SQL, matplotlib, etc.

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You will lead environmental and stress testing efforts, including temperature cycling, vibration, HALT, and HASS, conduct failure analysis and materials characterization, and analyze root cause investigations for manufacturing non-conformances and field returns. You'll participate in design reviews and FMEA activities, shape material selection and manufacturing requirements, analyze test and field data using reliability modeling tools, and help develop corrective actions and process improvements that elevate hardware reliability across the program.

Responsibilities: ude and implement design-for-reliability best practices, conducting rigorous testing, shaping manufacturing requirements, selecting materials, and analyzing field data to enhance the robustness of VBAT hardware. Perform stress screening, environmental testing, and drive failure analysis to ensure flight hardware meets reliability and performance targets. Analyze designs and test results to identify potential failure modes and mitigations. Collaborate with design engineers to implement design for reliability best practices early in design. Act as a key stakeholder in reviewing and approving designs for release. Participate in design reviews and failure mode effects analysis (FMEA) to assess potential reliability issues. Investigate manufacturing non-conformances and field hardware failures to determine root cause. Travel as needed to perform deep dives into supplier processes. Develop and recommend corrective actions to address identified reliability issues. Utilize reliability modeling and simulation tools to predict system performance and lifespan. Stay current with industry trends, advancements, and best practices in hardware reliability engineering. Propose and implement process improvements to drive improvements in reliability across the program.

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