AI Native, Data Scientist

ConsumerAffairs

About The Position

As an AI-Native Data Scientist at ConsumerAffairs, you will design and build AI-driven solutions that help us better understand consumers, improve marketplace performance, and accelerate business growth. You will partner closely with engineering, product, and business leaders to develop predictive models, generative AI applications, and data products—such as consumer sentiment models—that directly influence decision-making across the company. This is a hands-on, outcome-oriented role. You will work across the full lifecycle of AI and data science solutions—from identifying high-impact business opportunities and designing experiments to building models, deploying them into production, and iterating based on real-world performance. You will operate in a modern data environment that includes tools such as AWS, Databricks, Python, and large language model ecosystems. In this role, you will also evaluate and apply emerging AI technologies that increase productivity and unlock new capabilities. We are looking for someone who is both analytically rigorous and AI-native in mindset—someone who actively uses modern AI tools, embraces experimentation, and enjoys turning complex data into scalable, intelligent systems.

Requirements

Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or a related quantitative field, or equivalent practical experience.
Approximately 1-3 years of experience in data science, machine learning, or applied AI roles.
Strong experience with Python for data science and machine learning
Solid experience using SQL for querying and transforming large datasets
Experience building data pipelines and reproducible analytics workflows
Experience developing and deploying machine learning models using libraries such as scikit-learn, TensorFlow, PyTorch, or similar frameworks
Experience working with large language models (LLMs) and generative AI tools
Understanding of feature engineering, model evaluation, and experimentation frameworks
Experience working in cloud-based environments (AWS preferred)
Familiarity with Databricks or similar collaborative data platforms
Experience with version control (Git/GitHub) and collaborative development workflows
Exposure to ML operations practices, including model deployment, monitoring, and API development
Ability to communicate complex information in a direct and actionable fashion.
Strong analytical and problem-solving skills.
Strong verbal and written communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders.
Ability to prioritize tasks, meet aggressive deadlines, and work independently as well as collaboratively in a team environment.
Stands up for decisions, takes responsibility for results, and shares both good and bad outcomes transparently.
Demonstrates a relentless focus on results with a commitment to deliver; always starts with a positive attitude.
Takes decisive action, and confidently changes course if unsuccessful.

Nice To Haves

Experience working with modern AI APIs (e.g., OpenAI, Anthropic)
Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) architectures
Experience integrating AI models into production systems

Responsibilities

Partner with business and technical teams to identify opportunities where machine learning, predictive modeling, or generative AI can create measurable business impact.
Design, build, and deploy machine learning and AI solutions, including predictive models, recommendation systems, and LLM-powered applications that improve customer understanding and decision-making.
Develop scalable data pipelines and feature engineering workflows using tools such as Python, SQL, and cloud-based data platforms to support analytics and model development.
Apply modern machine learning techniques, including supervised and unsupervised learning, experimentation frameworks, and large language models (LLMs), to extract insights and automate complex tasks.
Own the model lifecycle, including training, evaluation, monitoring, and iteration, ensuring models remain reliable and impactful in production environments.
Contribute to ML operations workflows, including infrastructure configuration, API development for model access, and deploying models into batch or real-time production systems.
Leverage modern AI platforms and tools (e.g., OpenAI, Anthropic, vector databases, managed cloud ML services) to accelerate experimentation and product development.
Communicate insights and model results clearly through dashboards, visualizations, and presentations that translate technical findings into actionable recommendations.
Continuously evaluate new AI tools, frameworks, and methodologies to improve team productivity and unlock new capabilities for ConsumerAffairs.