Data Scientist, AI & Machine Learning Systems (Dublin, CA)

SavvyMoney•Dublin, CA

1d•Hybrid

About The Position

SavvyMoney is a leading San Francisco East Bay fintech company providing integrated credit score and personal finance solutions to over 1,600 bank and credit union partners nationally. The company offers a flexible hybrid work environment and is seeking a Data Scientist with strong Machine Learning Engineering capabilities to lead initiatives in predictive modeling, personalization, and AI-driven financial recommendations. This role involves adapting and deploying base models, integrating LLMs, and building scalable AI systems to power personalized marketing, loan offers, credit improvement strategies, next-best-action recommendations, and intelligent analytics experiences. The Data Scientist will collaborate closely with product and business teams to ideate and with engineering to deploy models into scalable, low-latency production environments.

Requirements

Master’s or PhD in Computer Science, Statistics, Data Science, or related field (or equivalent experience)
6+ years of professional experience in data science or machine learning, ideally in fintech, financial services, or a B2B2C environment
Strong proficiency in SQL and Python (pandas, scikit-learn, PyTorch, TensorFlow, XGBoost, etc.)
Hands-on experience with tree-based models (XGBoost, LightGBM, CatBoost) and neural networks
Proficiency in notebooks (Jupyter, Colab, etc.) and deep learning frameworks such as TensorFlow and PyTorch for model development
Familiarity with LLMs and generative AI frameworks (HuggingFace, LangChain, OpenAI APIs, etc.)
Experience deploying machine learning models into production environments with considerations for scalability and latency
Strong business acumen with the ability to translate complex analytical outputs into actionable recommendations
Excellent communication skills to collaborate with both technical and non-technical stakeholders
Legally authorized to work in the United States on a full-time basis without the need for employer sponsorship now or in the future.

Nice To Haves

Experience with cloud-based platforms (AWS, GCP, or Azure) for model training and deployment
Knowledge of MLOps tools and practices (MLflow, Airflow, Kubeflow, Docker, etc.)
Understanding of credit risk modeling, financial products, or consumer lending.
Experience working with APIs, real-time scoring, and event-driven architectures.

Responsibilities

Perform exploratory analysis to identify high-impact opportunities for AI-driven optimization and automation.
Design, build, fine-tune, and deploy machine learning models for: Marketing propensity modeling, Personalized loan offers and recommendations, Next-best-action and engagement optimization, Credit improvement and financial health predictions.
Adapt and fine-tune foundation models and LLMs for domain-specific use cases including recommendation engines, intelligent copilots, and conversational insights.
Partner closely with engineering teams to productionize models with strong considerations for latency, monitoring, reliability, and cost efficiency.
Help engineering build reusable ML frameworks, feature pipelines, and experimentation infrastructure to accelerate AI innovation.
Contribute to experimentation design (A/B testing, uplift modeling, bandits) to measure real business impact.
Work cross-functionally with Product and Business stakeholders to translate AI capabilities into measurable outcomes.
Implement model monitoring, drift detection, and performance tracking to ensure long-term reliability.
Follow responsible AI practices, ensuring fairness, transparency, and appropriate governance.