Senior Personalization Analytics Analyst

As a Senior Personalization Analytics Analyst at Kohl's, you will lead the development and testing of statistical and machine learning models, providing critical insights that support marketing strategies. Your role involves managing datasets, overseeing project communications, collaborating with IT teams, and developing dashboards to recommend actionable strategies to marketing leaders.

To be considered for the Senior Personalization Analytics Analyst position at Kohl's, a Bachelor's degree in a relevant field is required, along with experience in coding languages like Java, Python, or C++. A strong background in developing, testing, and curating statistical models, as well as familiarity with cloud offerings like AWS or Azure, will significantly strengthen your application.

Yes, the Senior Personalization Analytics Analyst position at Kohl's requires experience with cloud offerings, particularly those like Azure, AWS, or GCP. This expertise is crucial for executing data-driven marketing strategies and ensuring that your analytics solutions are robust and scalable.

Candidates for the Senior Personalization Analytics Analyst role at Kohl's should be comfortable coding in general-purpose languages such as Java, Python, or C++. Experience with statistical and machine learning model development is also vital to create insights that drive marketing strategies.

The Senior Personalization Analytics Analyst position at Kohl's offers a remote working option, however, candidates residing near our locations in Menomonee Falls, WI, or New York City, NY, are invited to work onsite from Monday to Thursday, with the flexibility to work remotely on Fridays.

I have developed and implemented various statistical models and machine learning algorithms, focusing on practical applications to enhance marketing strategies. For example, I recently constructed a predictive model that improved customer segmentation, leading to a 15% increase in campaign efficiency.

My approach to dataset curation involves thorough data cleaning, understanding the domain specificities, and collaborating with stakeholders to identify relevant features. I prioritize features based on their influence on the target outcome, often utilizing exploratory data analysis techniques.

In my previous role, I analyzed customer behavior data, which revealed unexpected trends in purchasing patterns. Presenting these insights led to a strategic shift in our marketing approach and resulted in a measurable boost in customer engagement.

I am proficient in using tools like GitHub for version control and project management, Trello for task organization, and Slack for communication among team members. These tools help streamline workflows and ensure smooth collaboration in project execution.

Handling conflicting feedback requires active listening and a structured approach to prioritization. I would engage stakeholders in a collaborative discussion to assess the impact of each piece of feedback on project outcomes, aiming for a consensus that aligns with organizational goals.

I prioritize clarity and simplicity in data visualization, often using tools like Tableau and Jupyter Notebooks. My strategy involves tailoring visuals to the audience, ensuring the insights are quickly digestible and actionable.

My experience with marketing teams includes leading analytics initiatives aimed at enhancing customer personalization. I frequently collaborate with marketers to develop models that provide tight alignment with their strategies and objectives, resulting in impactful recommendations.

I stay updated on industry trends and technologies through webinars, online courses, and networking with other professionals. I also subscribe to relevant journals and participate in relevant forums to continuously enhance my skill set.

I prefer using Python for model development due to its extensive libraries and frameworks available for data science. Libraries such as scikit-learn, TensorFlow, and pandas simplify the building and optimization processes.

To validate my models, I employ techniques like cross-validation to assess their performance on unseen data, alongside ROC curves and confusion matrices to gauge accuracy. This approach ensures my models are robust and reliable before deployment.