Lead Data Scientist
2 semanas atrás
.Your role as a Lead Data Scientist is to support stakeholders through data science techniques and building the solutions for the business's key initiatives.
In addition, you will leverage PandaDoc's data to enhance business performance and help stakeholders achieve their goals.
You will be assisting the business to continue its evolution into a data-driven culture and will apply and inspire the adoption of advanced data science across the business.You will report to the Director of Product & Growth Analytics and work closely with Product as well as Finance, Design, and Engineering.
As a Lead Data Scientist, you will take full ownership of your projects from start to finish.
This includes collaborating with stakeholders to understand key business questions, sourcing and preparing data, performing exploratory data analysis, building and fine-tuning models, and conducting in-depth analysis.
You will communicate your findings and insights through dashboards, visualizations, and presentations, ensuring alignment with business objectives.In this role, you will:Use data and apply creative analytical problem-solving skills to a wide variety of product questions to deepen our understanding of customer workflows, churn risk, and long-term value.Develop statistical models, machine learning algorithms, process automations, and predictive analytics solutions to address business challenges.Design experiments to test hypotheses and apply advanced statistical techniques and causal inference methods (i.E.
A/B testing, statistical tests, Bayesian experimentation, etc.)
to assess effectiveness of proposed changes.Assist the business and management team in the decision-making process using data analysis to make actionable recommendations.Understand business strategy and high-level roadmap and align analysis efforts to enable them with data insights and help achieve their strategic goals.Craft, deliver and own performance monitoring dashboards that guide understanding of how the business is performing today and where it may go next.Work with the data engineering team to get data pipeline processes productionized and automated.Example projects could include:Research and analyze different segments of our customer base to determine better product offering strategies.Develop a feature recommendation model to be deployed in product.Build predictive models focused on account adoption and expansion allowing us to improve customer experience, profitability, and forecasting.About you:7+ years hands-on professional experience in analytics working with large, complex, and multifaceted data sets and applying machine learning science techniques to solve practical business problems.7+ years of experience working with cleaning, manipulating, and visualizing data you can extract from multiple sources and an established data warehouse (Snowflake, Redshift, Oracle, Postgres, etc).Have a focus on creating repeatable work for models that are not unnecessarily complex
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