Data Science Lead

The Data Science Lead at SKIMS is tasked with owning the research, development, and delivery of data science and AI solutions that enhance critical business processes. This includes demand forecasting, customer lifetime value assessments, and operational analytics. You'll work closely with other leaders to develop analytical roadmaps that accelerate business growth, ensuring that data-driven insights are seamlessly integrated into our operations.

Candidates for the Data Science Lead role at SKIMS should ideally have 4-6 years of experience in data science or analytics, particularly within a direct-to-consumer context. A strong foundation in mathematics, statistical analysis, and proficiency in SQL and programming languages such as Python or R is crucial. Additionally, experience in a fast-paced startup environment and a BA/BS in a quantitative field is highly desirable.

SKIMS is dedicated to evolving its Data Science team by investing in building scalable methodologies and tools. The Data Science Lead will collaborate with data engineering to create production-grade pipelines and will also focus on enhancing data analytics competencies across the organization, fostering an environment where insights drive business strategies.

SKIMS prides itself on a vibrant and inclusive culture. For a Data Science Lead, this means being part of a team that values creativity, collaboration, and diversity. Perks like unlimited PTO, employee wellness programs, and a dog-friendly office reflect our commitment to maintaining a work environment that fosters innovation and employee satisfaction.

As a Data Science Lead at SKIMS, you’ll have numerous growth opportunities, including involvement in strategic initiatives and collaborations across departments. Leadership roles in innovative projects and the chance to influence company-wide data strategies will empower you to make impactful contributions while advancing your career.

When building a predictive model, I start with a clear understanding of the problem we're trying to solve. I focus on data selection, ensuring quality data collection, feature engineering, and considering the right algorithms for the task. Once the model is developed, I evaluate its performance using appropriate metrics and iteratively refine it to improve accuracy.

In my previous role, I analyzed customer purchasing patterns and identified key trends that informed our marketing strategy. By presenting these insights to stakeholders, we adjusted our promotional campaigns, which led to a significant increase in customer engagement and sales, demonstrating the direct impact of data on business choices.

I have hands-on experience with various data visualization tools such as Tableau and Power BI. I believe effective data visualization is crucial for translating complex data insights into actionable strategies. I always focus on clarity and tell a compelling story that resonates with stakeholders, enhancing decision-making processes.

In my work, I frequently employ regression analysis, clustering, and hypothesis testing as core statistical methods. These techniques allow me to derive significant insights from data, evaluate relationships between variables, and make data-driven predictions that are essential in guiding business strategies.

I stay current in the field of data science through continuous learning, attending relevant webinars, and participating in industry conferences. I also engage with online communities and read research papers to ensure that I remain aware of emerging techniques and technologies that could benefit my work.

I have extensive experience using SQL for data manipulation and retrieval, enabling me to extract valuable insights from large datasets. In my previous roles, I developed and optimized complex queries and collaborated with data engineers to ensure our database management practices support efficient data analysis.

When confronted with missing data, I first assess the extent of the missingness to understand its potential impact. I consider strategies such as imputation, where appropriate, and analyze the effects of deletion methods on my overall analysis. My approach is always data-informed, ensuring that the integrity of my analyses is maintained.

To explain complex data findings to non-technical stakeholders, I focus on simplifying the concepts using clear visuals and relatable examples. I ensure to emphasize the business implications of the data rather than the technical jargon, making it easier for all parties to grasp the significance of the insights drawn.

I have experience implementing various machine learning algorithms including supervised and unsupervised learning techniques, such as classification and clustering. I enjoy experimenting with different models and evaluating their performance to choose the one that best fits the data and problem at hand.

To manage multiple projects effectively, I prioritize based on urgency, impact, and alignment with business goals. I utilize project management tools to stay organized and ensure clear communication with my team, allowing us to remain agile and responsive while meeting our objectives.