Conseri is excited to partner with our client to identify value in their data assets and empower them with the technology, product, and go-to-market strategy to build scalable product offerings and execute towards successful commercial outcomes.
As a Data Scientist, you will work closely with Engagement Managers who lead client projects focusing on building scalable data solutions. You will utilize your demonstrated expertise in data analytics and strong attention to detail to execute exceptional deliverables that instill confidence in your work from both internal stakeholders and clients.
Responsibilities:
- Develop deep business expertise, with a focus within one business unit, and apply business knowledge to unlock value in data.
- Own all phases of model lifecycle, including data preparation, model development, testing, deployment, and monitoring.
- Build and maintain ML and statistical models to:
- Automate/support actuarial workstreams, including building and improving underwriting guidelines and pricing, supporting go/no-go decisions on new books of business, reserving, etc.
- Automate decision making across the business e.g. when to investigate a claim.
- Forecast trends in programs, business units, and the insurance industry as a whole, to inform business strategy.
- Build and maintain LLM pipelines to leverage unstructured data e.g. read policy documents to determine if a claim is covered, summarize policy documents, etc.
- Build and maintain data pipelines in Python to clean, validate, and prepare data for ingestion into models & analysis, and to analyze/monitor outputs of models & analysis.
- Support development and maintenance of production model hosting, CI/CD pipelines, ML Ops infrastructure, and model monitoring pipelines.
- Perform ongoing research; advise on best practices for data science modeling and infrastructure.
- Collaborate with business and technical stakeholders to build requirements and deliver value.
- Develop strong customer focus, ownership, urgency, and drive.
Core Competencies:
- 5 yrs in Data Science, Computer Science, Engineering or equivalent required; Masters preferred.
- Experience building machine learning models with financial data, preferably with insurance data.
- Experience across all phases of model lifecycle, including data wrangling, model development, testing, deployment, and monitoring.
- Experience with cloud model infrastructure / MLOps, preferably in Azure.
- Strong knowledge of Python (incl. Pandas) and SQL.
- Experience working with messy data.
- Experience with Databricks, PySpark, dbt, Snowflake is a plus.
- Azure certification is a plus.
- Experience with LLMs (langchain, Azure OpenAI, vectorstore dbs) is a plus.
- Excellent written and verbal communication skills.
Our client is unable to transfer visas at this time.
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