Data Scientist - Growth/Marketing Analytics

At Kikoff, a Data Scientist in Growth and Marketing Analytics is responsible for leading initiatives that drive user growth through in-depth analysis of user behaviors. This includes developing attribution models, conducting customer journey analyses, and designing experiments to optimize marketing strategies, ultimately enhancing the effectiveness of marketing campaigns.

Candidates applying for the Data Scientist role at Kikoff should possess a Bachelor's degree or higher in a quantitative field such as Statistics, Applied Mathematics, Economics, or Computer Science. Additionally, 3-5 years of experience in analytics to influence growth and marketing strategies is essential, as well as strong knowledge of SQL and Python.

A Data Scientist at Kikoff plays a crucial role in driving user growth by employing data analysis to inform customer acquisition strategies. By building predictive models and analyzing customer journeys, you can identify areas for optimization and implement strategies that significantly enhance user engagement and retention.

Absolutely! At Kikoff, we prioritize professional growth and development. Working closely with seasoned entrepreneurs and a talented team, you will have ample opportunity to learn, refine your skills, and take on greater responsibilities, making it an ideal environment for your career growth.

Kikoff is an exciting workplace for Data Scientists because we are a fast-paced startup that values innovation and ownership. Our data-driven culture means you'll have a direct impact on our financial wellness mission, while also enjoying competitive benefits and a flexible work environment in a supportive team.

As a Data Scientist at Kikoff, you'll primarily work with SQL for data manipulation and Python for analysis and model building. Additionally, you will use various experimental design techniques, dashboards, and reporting tools to track and analyze marketing effectiveness.

While Kikoff is headquartered in San Francisco, we recognize the importance of flexibility and may offer remote work options on a case-by-case basis. We encourage interested candidates to discuss their preferences during the interview process.

When building an attribution model, I start by clearly defining the marketing channels involved and the key metrics of success. I utilize historical data for each channel and apply statistical methods, such as regression analysis, to assess their impact on conversions and user acquisition. It’s essential to consider external factors and ensure the model is adaptable to changing conditions.

For customer journey analysis, I employ techniques like cohort analysis and funnel analysis to understand each phase—awareness, engagement, and conversion. I leverage data visualization tools to present insights, helping the team identify drop-off points and opportunities for optimizing the user experience.

To measure ROI of marketing efforts, I gather data on campaign costs, conversions, and associated revenue. Using this data, I calculate the ROI by comparing the net profit against the overall marketing expenditure. I also look into customer lifetime value to determine long-term ROI, giving us a complete picture of marketing effectiveness.

In my experience with A/B testing, I start by formulating a clear hypothesis regarding the changes I want to test. I then define the control and variant groups, ensuring a statistically significant sample size. Throughout the campaign, I monitor results in real-time, analyze the data post-campaign, and draw actionable insights to refine future marketing strategies.

Stakeholder engagement is vital in my role as a Data Scientist because meaningful collaboration ensures that analytical insights align with business objectives. I prioritize clear communication, sharing findings in an easily digestible format, and actively engaging with teams to understand their needs and feedback, fostering a data-driven culture.

Staying updated with the latest statistical methods involves reading research papers, following thought leaders in data science and analytics, and participating in relevant workshops and conferences. Additionally, I find online courses and webinars invaluable for learning new software tools or methods to enhance my analytical capabilities.

A significant challenge I've faced in marketing analytics is dealing with incomplete or inconsistent data. To mitigate this, I implement thorough data cleaning processes and transform raw data into reliable datasets. Collaborating with interdisciplinary teams helps to ensure the integrity and accuracy of our data source prior to analysis.

One successful project I led involved analyzing user engagement metrics across various marketing channels. Through deep dive analysis, I identified key drivers of customer retention and recommended targeted re-engagement campaigns. This resulted in a 20% increase in user retention over three months, significantly impacting our marketing strategy.

I have extensive experience with customer segmentation, where I apply clustering and propensity modeling techniques. By identifying distinct customer segments based on behavior and preferences, I can recommend personalized marketing strategies that effectively engage that segment, thereby improving conversion rates and customer satisfaction.

To prioritize growth initiatives, I evaluate their potential impact based on data analytics, assessing cost versus benefit and feasibility. I often create a scoring model that weighs various factors, including estimated ROI, alignment with business goals, and existing resources, allowing for data-driven decision making.