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Sr. Data Science Engineer, Realm-X

AT AppFolio
AppFolio

Sr. Data Science Engineer, Realm-X

Remote
Hi, We’re AppFolio

 

We’re innovators, changemakers, and collaborators. We’re more than just a software company – we’re pioneers in cloud and AI who deliver magical experiences that make our customers’ lives easier. We’re revolutionizing how people do business in the real estate industry, and we want your ideas, enthusiasm, and passion to help us keep innovating.

What we’re looking for 

We are looking for a Sr. Data Science Engineer to join the Data Science & Analytics Department. You will be instrumental in developing data models for our chat-to-data skill in Realm-X Assistant, 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, and contributing to our Machine Learning codebase for transforming users' natural text queries into data/insights.

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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 of data structures that cater to multiple enterprise-scale products and applications.
  • Evaluate the performance of LLMs in different experimental conditions with variations in training data, fine-tuning techniques, or model architectures.
  • Embed with a team of software engineers, data engineers, and ML Engineers to build the foundational data models that power LLMs
  • Produce and build tracking of various product success metrics related to conversational quality, coherence, relevance, and user satisfaction.


Must have 
  • Minimum of 5+ years of work experience in a technical role, Analytics/Data Engineering or Data Science.
  • Full Stack Data Science/Engineering: experience in storing, moving, transforming, analyzing, and presenting data with the ability to understand and solve the types of challenges presented to those working in each part of the data stack
  • Problem Solving & Project Management Skills: Demonstrated success in bridging the gap between high-level project requirements and complex application data
  • Data/Analytics Engineering: Expertise in DBT data modeling, object-oriented programming, and the technical skills to build and deploy model pipelines to production.  Experience creating/editing widely used production tables.
  • Data analysis, visualization, and exploration: Exploratory data analysis skills are a critical tool for data science engineers, and the results help answer important questions.
  • Proficiency in SQL and Python-based (Pandas, Numpy, and/or Spark) approaches for data transformation.
  • Modern Methods: experience with AWS/Cloud technologies, version control, and linux commands
  • Communication: You have a wealth of experience collaborating with technical and non-technical teammates on complex data projects

Nice to have 
  • Experience working with product data at a software company
  • Familiarity with Property Management/Real Estate Industry
  • Experience with Kafka, Flink, and/or CDC pipelines
  • LLM Prompt Engineering Experience

 

Compensation & Benefits
The base salary/hourly wage that we reasonably expect to pay for this role is: $138,400-$173,000
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.

 

#LI-KB1

 

Job ID: ocBlvfwn-CtbKYfw6
Employment Type: Other