Note: The job is a remote job and is open to candidates in USA. System Soft Technologies is seeking a Lead Data Scientist to develop and implement commercially viable model-based solutions for healthcare, insurance, life sciences, and adjacent markets. The role involves developing traditional AI/ML and generative AI solutions, collaborating with product and business teams, and driving data science best practices and continuous improvement.
Responsibilities
- Research, develop, deploy, and maintain traditional AI/ML models following industry best practices
- Work extensively with available GenAI models; construct exciting solutions to internal and external use cases across markets and enhance our internal capabilities through centralized internal tooling and thought leadership
- Coordinate with Product, Business Development, ML Engineering, and IT to bring new and exciting data science products to market, as well as support existing industry leading products
- Help drive best practices and continuous improvement on the data science team; influencing model design and experimentation strategy through planning, audit, peer review, and other coaching
- Excellent communication skills, in person and through phone / email
- Proven ability to understand client analytical needs, translate them into an action plan, then execute and deliver
- Customer-centric approach to finding solutions
- Focused on results and able to explain findings in a way that answers business problems
- Constructive, “can do” approach to overcoming obstacles
- Can quickly learn new techniques and technologies
- Proactive in identifying process improvements
- Strong work ethic, willing to pitch in wherever needed
- Thrive in a small team, without micromanagement
- Ability to manage projects and timelines, including directing the work of others
Skills
- • Excellent communication skills, in person and through phone / email
- • Proven ability to understand client analytical needs, translate them into an action plan, then execute and deliver
- • Customer-centric approach to finding solutions
- • Focused on results and able to explain findings in a way that answers business problems
- • Constructive, “can do” approach to overcoming obstacles
- • Can quickly learn new techniques and technologies
- • Proactive in identifying process improvements
- • Strong work ethic, willing to pitch in wherever needed
- • Thrive in a small team, without micromanagement
- • Ability to manage projects and timelines, including directing the work of others
- • 10+ years of professional experience using AI/ML to create high return on investment commercial data science solutions
- • Expertise with Electronic Health Records or unstructured data analysis
- • Expert data scientist with demonstrable capability building traditional AI/ML models
- • Supervised Learning: Linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), and ensemble methods
- • Unsupervised Learning: K-means clustering, hierarchical clustering, PCA, and anomaly detection algorithms
- • Model Validation: Cross-validation strategies, hyperparameter optimization (Grid Search, Random Search, Bayesian optimization), and A/B testing frameworks
- • Deep Learning Architectures: Neural networks, transformers, and transfer learning methodologies
- • NLP Algorithms: Text preprocessing, TF-IDF, word embeddings (Word2Vec, GloVe), topic modeling (LDA), sentiment analysis, and named entity recognition
- • Expert understanding of NLP and generative AI; able to effectively use, fine-tune, and evaluate commercially available models as well as deploy and integrate local LLMs into the data science process
- • Hands-on experience building GenAI applications (e.g., RAG systems, LLM evaluation frameworks, or GenAI-powered internal tools)
- • Expert level Python programmer, with some experience in R and/or SQL
- • Expert user of Databricks or similar cloud-based model development ecosystem including mlflow, experimentation organization, data catalogs, and compute cluster configuration
- • Sufficient understanding of software engineering best practices such as Git for version control, unit testing, local development, and environment management
- • Knowledge of ML Engineering and ML Ops related concepts and tools including CICD pipelines, GitHub Actions, Docker, AWS Lambda, and Linux
- • Degree in a relevant field (computer science, data science, statistics, mathematics, applied math, actuarial science, economics, etc.)
- • PhD in relevant field or Actuarial designation (FCAS/FSA)
- • Experience in one of the following industries: healthcare, insurance (L&H or P&C), finance, life sciences, or similar fields
- • Experience at an InsurTech or FinTech
- • Past experience working in a HIPAA / PHI / PCI compliant environment
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