Data Scientist

Job Description:

Company Overview

Homethrive was born from personal experience. Our founders grappled with the overwhelming challenges of caregiving for family members while balancing their work lives. The journey was fraught with confusion, a myriad of unanswered questions, and countless hours delving into endless online searches. After taking numerous days off and spending extended hours on the phone, the answers remained elusive. They recognized the need for a streamlined, more efficient solution. Enter Homethrive! 

Our Mission

Homethrive transforms aging and caregiving through personalized, human-centered support that bridges gaps in care and reduces social isolation. Through our Virtual Companionship Program and dedicated Care Team Members, we build meaningful connections that empower seniors to age confidently while providing essential support to caregivers and families.

Position Overview

Homethrive is growing, and we need to bring on additional talent to help us on our growth journey! We are looking for a Data Scientist who will be responsible for collecting, analyzing, and interpreting complex data sets to drive business decisions and strategies. We are looking for someone with strong hands-on experience in all layers of data integration, analytics, and ML/AI! 

The technology we currently utilize includes:

• Python

• Snowflake

• AWS RDS (MySQL), MongoDB Atlas

• Salesforce CRM, Tableau

• Cloud hosting on AWS using a mixture of Lambda, Glue, DynamoDB, and S3

Key Responsibilities:

1. Data Collection and Preprocessing:

• Acquire, clean, and transform structured and unstructured data from various sources.

• Identify and address data quality issues, missing values, and outliers.

• Develop and maintain data pipelines and data warehousing solutions.

2. Exploratory Data Analysis and Modeling:

• Perform exploratory data analysis to identify patterns, trends, and relationships within data sets.

• Design and implement statistical and machine learning models to solve complex business problems.

• Evaluate and optimize model performance using appropriate techniques and metrics.

3. Data Visualization and Storytelling:

• Create compelling data visualizations and dashboards to effectively communicate findings and insights.

• Collaborate with cross-functional teams to translate data-driven insights into actionable recommendations.

• Present complex analytical results to both technical and non-technical audiences.

4. Model Deployment and Monitoring:

• Deploy and integrate machine learning models into production environments.

• Monitor model performance, identify potential issues, and iterate on models as needed.

• Collaborate with engineering teams to ensure smooth integration and scalability of data solutions.

5. Generative AI, Large Language Model (LLM), and Chatbot Technologies:

• Explore and implement novel approaches for fine-tuning and adapting LLMs to specific domains, tasks, and use cases.

• Collaborate with our engineering teams to integrate LLM models into production systems and chatbot interfaces.

• Conduct research and experimentation to enhance the performance, safety, and robustness of our generative AI models.

6. Continuous Learning and Innovation:

• Stay up-to-date with the latest developments in data science, machine learning, and related technologies.

• Explore and experiment with new techniques and tools to enhance data-driven decision making.