• Contribute to the design and build intelligence systems that help Liquid Engine understand workflow context, track change over time, and support consistent outputs • Translate creative and production workflows into structured representations such as entities, relationships, rules, and graphs • Help develop systems for tracking state, version history, provenance, and dependencies across workflows • Build rule-based and heuristic systems that identify conflicts, evaluate consistency, and surface issues before they create downstream problems • Support change impact analysis by helping the system reason about dependencies, cascades, and workflow consequences • Develop evaluation frameworks that measure system usefulness, output quality, and reduction of manual rework • Partner with engineering on event pipelines, data models, and system architecture that support intelligent system behavior • Contribute to explainable recommendation and decision-support systems grounded in workflow context • Help define telemetry, measurement, and governance approaches for intelligence features across the platform

• Own the end-to-end lifecycle of production machine learning models, from problem definition through deployment and ongoing optimisation. • Design, build and deploy artificial neural network and machine learning models for vehicle valuation, pricing and other analytics. • Take responsibility for production model performance, reliability and long-term maintenance. • Evaluate model performance and improve predictive accuracy across production models. • Develop and maintain automated retraining pipelines to keep models effective over time. • Monitor deployed models, investigate issues and implement improvements to ensure models remain accurate and reliable. • Design and run experiments, track results and use data to drive model improvements. • Work closely with engineering and product teams to integrate models into production systems and deliver business value.

• Provide data management support by assisting with data processing, including downloading, processing, cleaning, documenting, consolidating, archiving, and transferring of data in various files formats. • Provide data management support to by posting of data in various file formats and locations. • Document the development and implementation of data collection methods and protocols. • Manage and update Power BI dashboard. • Experience modernizing surveillance systems, including but not limited to converting SAS code to Python/R. • Automate reoccurring reports, tables, and presentations of data for the team to access on a routine basis. • Create and manage quality assurance programs for daily downloads from website. • Provide analytic support including regular and ad hoc epidemiologic and statistical analyses. • Produce reports, tables, and presentations of the data.

Role Description Immediate Full-Time Position for Senior Data Scientist (This will be FTE only). - Design, develop, and deploy end-to-end AI/ML solutions for predictive modeling, risk stratification, behavioral analytics, and treatment pathway prediction. - Build advanced NLP, LLM, and RAG-based applications, including prompt engineering, fine-tuning, and clinical AI guardrails for extracting insights from unstructured data. - Develop speech AI and conversational AI capabilities using ASR, sentiment analysis, intent classification, Text-to-SQL, and decision-support models to improve care outcomes. - Measure model performance through A/B testing, cohort analysis, explainability (SHAP/LIME), drift monitoring, and HIPAA-compliant governance. - Collaborate with Data Engineering to implement feature stores, MLflow, MLOps, CI/CD pipelines, and scalable real-time/batch inference solutions. - Mentor junior data scientists, evaluate emerging AI technologies, and drive the AI roadmap by translating advanced models into actionable healthcare solutions. Qualifications - 7+ years of hands-on data science experience building and deploying predictive analytics, NLP, and Generative AI solutions in production, preferably within healthcare or other regulated industries. - Proven expertise across the entire machine learning lifecycle, including feature engineering, model development, deployment, monitoring, and optimization. - Advanced Python skills with pandas, NumPy, scikit-learn, PyTorch, and TensorFlow, along with classical ML techniques such as XGBoost, LightGBM, survival analysis, time-series forecasting, and deep learning. - Strong experience with LLMs, RAG architectures, transformer models (BERT/GPT), Hugging Face, LangChain, vector databases, and LLM fine-tuning and evaluation. - Hands-on expertise with NLP, speech AI, Databricks (Delta Lake, MLflow, Spark), AWS (S3, SageMaker, Bedrock, Redshift, Athena), SQL, Docker, Kubernetes, and MLOps practices. - Demonstrated leadership through mentoring, cross-functional collaboration, and the ability to communicate complex AI/ML insights to technical and business stakeholders while driving innovation and best practices. Company Description