Data Scientist

Ledgent Technology; Swanston (CA); On-site; USD 77,000 - 107,000; Full time

Job summary

Ledgent Technology in California is seeking a Data Scientist to design, build, validate, and deploy data science and AI solutions that enable data-driven decision making across business operations. The role partners with business and technology stakeholders to identify opportunities and translate quantitative findings into actionable insights.

Within the Data & Technology Solutions division, you will contribute to enterprise data platform development, data science capabilities, and AI tooling,

Qualifications

• Experience designing and deploying data science and AI solutions.
• Strong programming in Python or R; familiarity with ML libraries.
• Experience with SQL and cloud platforms.
• Ability to translate complex findings for diverse stakeholders.

Responsibilities

• Serves as a visible ambassador for AI and data science across the organization.
• Develops, validates, and deploys analytics & data science models and agentic AI solutions.
• Designs proof-of-concept frameworks with defined success criteria and evaluation metrics; scales POC into production-grade pipelines.
• Translates model outputs into actionable decision-support tools for technical and non-technical audiences.
• Collaborates with data engineering to define data ingestion, feature engineering, and model serving requirements.
• Partners with stakeholders to identify and deliver data science use cases and assess third-party analytics tools.

Skills

Python

R

SQL

ML Libraries

Pandas

Azure Databricks

MLOps

Power BI

Tableau

Tools

GitHub

Azure DevOps

Power BI

Tableau

Job description

Salary: USD56 - USD78 per hour

The Data Scientist position is responsible for designing, building, validating, and deploying data science and AI solutions that enable data-driven decision‑making across business operations.

The Data Scientist partners with business and technology stakeholders to identify opportunities, develop data science and machine learning models or AI solutions, and translate complex quantitative findings into actionable insights.

This role sits within the Data & Technology Solutions division and contributes to the build‑out of enterprise data platform, data science, and AI capabilities.

Responsibilities

• Serves as a visible ambassador for AI and data science across the organization.
• Develops, validates, and deploys analytics & data science models and agentic AI solutions using structured and unstructured data to address business challenges across operations, asset management, finance, and other functional areas.
• Designs proof‑of‑concept frameworks with defined success criteria and evaluation metrics; scales successful POCs into repeatable, production‑grade pipelines within data platform.
• Translates model outputs into actionable decision‑support tools and communicates findings clearly to both technical and non‑technical audiences, including senior leadership.
• Collaborates with data engineering to define data ingestion, feature engineering, and model serving requirements; documents model logic, assumptions, data dependencies, and performance benchmarks.
• Partners with business and technology stakeholders to identify, scope, and deliver data science and AI use cases; provides technical assessments of vendor‑built models and third‑party analytics tools as needed.

Specific Job Requirements

• Successful completion of pre‑employment drug, alcohol, and background investigation.
• Ability to build trust and rapport quickly with employees at all levels, demystifying AI for non‑technical audiences, and championing responsible adoption through day‑to‑day partnership.
• Strong programming skills in data science and AI/ML languages (e.g., Python or R), with fluency in core ML and AI libraries such as scikit‑learn, XGBoost/LightGBM, and pandas.
• Skilled at leveraging pre‑built and foundation models rather than building from scratch where appropriate; demonstrated ability to design and build agentic AI solutions (e.g., tool use, retrieval, multi‑step orchestration) with appropriate guardrails and evaluation.
• Proficiency with SQL and cloud data platforms (e.g., Azure, Databricks, or equivalent); comfort working with large‑scale structured and semi‑structured datasets.
• Deep understanding of model validation, evaluation metrics, feature engineering, and ability to identify and mitigate data leakage.
• Ability to explain findings, model performance and results, and recommendations clearly to diverse audiences and translate business problems into analytical frameworks.
• Ability to preserve confidential and proprietary information and successfully avoid conflicts of interest.
• Experience working effectively in cross‑functional teams and establishing positive working relationships across functions and levels.
• Experience with MLOps/AIOps practices, version control tools (e.g., GitHub, Azure DevOps), Agile software development best practices, AI‑assisted coding tools (e.g., Claude Code) to accelerate development; and data visualization platforms (e.g., Power BI, Tableau) preferred.
• Must be able to prioritize, manage multiple workstreams, and recognize when to seek direction.