Sr. Data Scientist

As a Sr. Data Scientist at Measured, your primary responsibilities include developing advanced Marketing Mix Models (MMM), integrating various media and economic factors into your analysis, and implementing innovative solutions. You will work cross-functionally with various teams to enhance our data-driven capabilities, automate model processes, and support marketing measurement. You’ll also translate complex data into actionable insights for our clients, providing them with the intelligence needed to maximize ROI.

To be considered for the Sr. Data Scientist position at Measured, you need a B.S. or higher in Mathematics, Computer Science, or related fields, along with 5+ years of data science experience, particularly in marketing analytics. Experience in building Marketing Mix Models and employing statistical techniques, including Bayesian inference, is essential. Strong programming skills in Python and SQL, outstanding problem-solving abilities, and excellent communication skills to convey complex concepts to diverse audiences are also key qualifications.

The Sr. Data Scientist role at Measured promotes professional growth by providing opportunities to work with advanced analytical methodologies and collaborate with cross-functional teams. You’ll engage in R&D of cutting-edge measurement techniques, attend training sessions, and have access to a culture of knowledge sharing, all aimed at enhancing your skills. Additionally, our values of curiosity and innovation encourage you to take risks and explore new avenues in your work.

To excel as a Sr. Data Scientist at Measured, you should possess a strong foundation in statistical modeling, proficiency in Python and SQL, and expertise in Marketing Mix Modeling. Additionally, problem-solving skills and the ability to work independently in a fast-paced environment are crucial. You should also be able to communicate complex mathematical concepts clearly to both technical and non-technical audiences, ensuring that your insights effectively inform business decisions.

The work culture for Sr. Data Scientists at Measured is dynamic, inclusive, and oriented toward innovation. We value diversity and integrity, ensuring a collaborative environment where your voice is heard. With flexible work arrangements and a commitment to employee well-being, Measured supports personal and professional development. You'll be part of a team dedicated to reshaping marketing measurement through creativity and data-driven insights.

When developing a Marketing Mix Model, start by identifying key variables such as media spend, pricing, and promotional activities. Collect historical data, then apply statistical techniques like regression analysis. It's essential to utilize causal inference to determine the impact of different factors on sales, ensuring the model is robust. Finally, validate your model through back-testing and continually refine it based on new data.

In my previous roles, I have employed Bayesian inference to enhance the accuracy of predictive models. This involved incorporating prior knowledge and continuously updating probabilities as new data became available, which is crucial for developing uncertain models like MMM. I find that Bayesian techniques can effectively capture the complexity of real-world scenarios, yielding more reliable insights into marketing performance.

I approach working with large-scale data sets by first ensuring data quality through cleaning and preprocessing. I prioritize efficient data storage and retrieval methods, utilizing tools like SQL for querying. When analyzing, I employ distributed computing techniques where necessary to handle large volumes effectively. Finally, I focus on optimizing algorithms to ensure that the insights extracted are both timely and meaningful.

In a past role, I was tasked with presenting the results of an MMM analysis to a marketing team with varying technical backgrounds. I used visual aids to simplify the data, translating statistical concepts into practical implications for their marketing strategies. I focused on storytelling, linking data points to real-world applications, which allowed them to understand and act upon the insights with confidence.

To ensure the reliability of my data models, I routinely perform validation checks, such as cross-validation and back-testing. I monitor model performance metrics and analyze residuals to check for inconsistencies. Furthermore, I continuously retrain models with new data and update them based on changing market dynamics, ensuring that they adapt and remain accurate over time.

I keep up with the latest trends in data science and marketing analytics through a combination of reading industry publications, attending webinars, and participating in professional networks. Engaging in online courses and conferences also helps me expand my knowledge and connect with thought leaders. This commitment to continuous learning allows me to incorporate innovative practices into my work.

Once, I was faced with a complex marketing attribution challenge, where multiple channels were involved, and traditional models fell short. I implemented a combined approach using MMM and machine learning techniques that allowed for a clearer understanding of each channel's contribution. This comprehensive analysis enabled the marketing team to optimize their media spend significantly.

Automation plays a critical role in my data science processes by streamlining model training, deployment, and monitoring. By automating repetitive tasks, I can focus more on analysis and deriving insights. Automation also improves efficiency and reduces the likelihood of human error, ensuring that I can deliver timely and accurate results to stakeholders.

Handling ambiguity in data science projects involves embracing uncertainty and being adaptable. I prioritize defining the problem clearly and asking the right questions to gather the necessary information. Iterative experimentation can also help, allowing me to refine my approach as new insights emerge. Clear communication with stakeholders ensures alignment on objectives, even in uncertain environments.

Diversity is crucial in data science teams because it fosters varied perspectives that can lead to more innovative solutions. Different backgrounds contribute to creative problem-solving and enhance the understanding of complex data sets, ensuring that the insights generated are more comprehensive and relevant. A diverse team reflects the multi-faceted world we analyze, ultimately driving better business outcomes.