Data Scientist - ApartmentIQ

Position Overview

We are seeking a skilled Data Scientist to join our ApartmentIQ data team. This role involves using analytical skills to improve the accuracy of our data, and enrich our data set with new attributes, collaborating with product, engineering, and customer experience teams to deliver product capabilities. Success in this fast-paced environment requires strategic thinking, strong organizational and communication skills, and the ability to manage multiple challenges. Autonomy, curiosity, and the ability to make recommendations in uncertain situations are essential.

Company Information

We’re a profitable, growth-stage company building industry-leading martech and data products for the rentals industry. While originally known for building and operating one of the U.S.’s largest rental marketplaces - Rentable, our focus has shifted to our category-leading AI and data SaaS products with triple-digit growth rates.

We’re a fully remote team of 100+ spread across the U.S. from coast to coast. We operate on a strict no a**holes policy and are proud to have built a community of highly performing people that take our work seriously, but not ourselves.

While we’ve raised $30MM+ to date from some of the world’s best investors, we’re profitable with a strong balance sheet and an indefinite runway. We pride ourselves on achieving rapid growth without having to incinerate capital.

If you like the idea of joining an industry-changing company made up of people who genuinely like each other, Rentable could be a great fit for you.

Responsibilities

• Develop and deploy predictive models using machine learning algorithms (e.g., regression, decision trees, random forests, neural networks) to forecast trends, optimize decision-making processes, and improve business outcomes.
• Apply advanced statistical techniques and machine learning algorithms to analyze large datasets, identify patterns, and predict future trends.
• Develop and optimize models in Python using machine learning, and statistical techniques, leveraging packages such as XGBoost, and scikit-learn to build accurate predictive models for business decision-making.
• Design and implement scalable LLM-based classification pipelines to extract and standardize property attributes, ownership structures, and asset classes across multifamily real estate datasets.
• Leverage state-of-the-art NLP techniques and foundation models (e.g., GPT, Claude, or open-source LLMs) to automate property classification, entity resolution, and enrichment using unstructured data sources such as listings, legal records, or marketing content.
• Create data visualizations, dashboards, and reports to communicate insights to both technical and non-technical stakeholders.
• Collaborate with product and engineering teams to integrate your solutions into the product, assess model performance (accuracy, precision, recall, AUC-ROC), and refine algorithms over time.
• Lead discussions and presentations on data-driven insights with stakeholders, showing your work and facilitating decision-making across key stakeholders.

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field.
• 3+ years of experience as a Data Scientist, demonstrating a strong ability to leverage data in business settings.
• Proficient in Python, and familiar with libraries for data manipulation and analysis.
• Solid understanding of statistical concepts and machine learning algorithms (e.g., forecasting, regression, classification, clustering).
• Experience deploying LLM pipelines at scale.
• Skilled in data visualization tools (e.g., Matplotlib, Seaborn) and big data technologies (e.g., Spark).
• Experience with SQL, database technologies, deep learning frameworks (e.g., TensorFlow, PyTorch), and NLP techniques.
• Relevant certifications in machine learning, data science, or cloud computing are a plus.
• Excellent organizational skills.

Employment Type

• [Employment Type Not Specified]

Location Type

• [Location Type Not Specified]

Salary

• [Salary Not Specified]