AppFolio is more than a company. We’re a community of dreamers, big thinkers, problem solvers, active listeners, and multipliers. At every opportunity, we set the pace while delivering innovation built to carry real estate into the future. One in which every experience feels effortless, yet meaningful. Where customers are empowered to take on any opportunity. We show up as one team, connected by our values to be a force for good. Because together, we have the power to create extraordinary outcomes for our customers, our communities, and ourselves
What we’re looking for
We are looking for a Sr. Data Scientist to join the Data Science & Analytics Department. You will be instrumental in developing data models for our chat-to-data skill in Realm-X, structuring our product data in the most optimized fashion for customer and LLM interpretation, evaluating model performance and crafting success metrics of this novel AI product offering.
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Your impact
- Perform data discovery of stakeholders' needs, help design solutions, and collaborate with engineering on how their infrastructure should affect data transformation design.
- Design and enhance data models, schemas, quality testing, and database designs to support efficient data streaming in production.
- Partner where applicable to develop data documentation, including data dictionaries, data lineage, and integration processes.
- Collaborate with Engineering and Product teams to ideate on permutations data structures that cater to multiple enterprise-scale products and applications.
- Produce insights (e.g. performance drivers, retention analysis, behavioral personas) to accelerate adoption; this requires acquiring and cleaning data from multiple sources, structuring and building data models, analyzing to generate insights, and distributing them.
- Evaluate the performance of the LLM in different experimental conditions, such as variations in training data, fine-tuning techniques, or model architectures.
- Produce and build tracking of various product success metrics related to conversational quality, coherence, relevance, and user satisfaction.
Must have
- Minimum of 4+ years of work experience in a Product Data Science role.
- Full stack Data Science experience: Demonstrated success in bridging the gap between high-level project requirements and complex application data
- Data engineering: familiarity with engineering best practices and skills to build and deploy model pipelines to production.
- Data analysis and exploration: Exploratory data analysis skills are a critical tool for every full stack data scientist, and the results help answer important business questions.
- Applied Statistics - Statistical modeling and support of production systems. Understanding of statistical concepts and practical experience applying them (A|B testing, causal inference ).
- Proficiency in SQL and Python-based (pandas or pyspark) tooling for data transformation.
- Communication - You have a wealth of experience helping product teams make great decisions with data.
- Strong Data Visualization skills: Streamlit, Tableau, (Looker, PowerBI)
Nice to have
- Data modeling experience using dbt and other advanced data pipeline tooling
- Experience building data products
- Familiarity with machine learning (model training/evaluation, ML applications)
- Experience building effective data visualizations with tools like Streamlit or Tableau
Compensation & Benefits
The base salary/hourly wage that we reasonably expect to pay for this role is: $123,000-$185,000
The actual base salary/hourly wage for this role will be determined by a variety of factors, including but not limited to: the candidate’s skills, education, experience, etc.
Please note that base pay is one important aspect of a compelling Total Rewards package. The base pay range indicated here does not include any additional benefits or bonuses/commissions that you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.
Regular full-time employees are eligible for benefits - see here.