Senior Data Scientist II

Emburse Senior Data Scientist II - Data Science Platform
As a Senior Data Scientist II at Emburse, you will drive design and implementation of data products using a variety of AI approaches including foundation models (GenAI), deep learning, neural networks and other machine learning pipelines to help build the future of full expense lifecycle automation.
Description
As a key member of our team, you will support our product development teams with insights gained from analyzing company data with respect to potential opportunities for product and process optimization. You must have strong experience using a variety of data mining/data analysis methods. The Senior Data Scientist II will lead data modeling and machine learning projects using a variety of data tools, modeling approaches and algorithms. You will also design experiments to evaluate models by conducting AB testing and simulations.
Key Responsibilities
● Able to translate product design requirements into pipeline of heuristic and stochastic algorithms
Understand exploratory data analysis for product feasibility studies and ground truth testing
Execute SQL queries and/or python scripts to manipulate, analyze and visualize data
Able to implement explainable AI solutions and rationalize model inferences
Follows SDLC processes, Adopt agile-based processes/meetings and peer code-reviews
Works with Machine learning engineer/architect to deploy data products into production
Follows and understands legal data use restrictions
Contributes to algorithm library development and design for ML, NLP and XAI
Delivers product pipelines for deployment to production
Builds applications that integrate third party and self-hosted foundation models.
Fine tunes open source foundation models (LLMs, VLMs) with proprietary data
Develops autonomous AI inference and tool use orchestration using ReAct AI agents
Provides root cause analysis for machine learning model inference
Completes data analysis or processing tasks as directed
Documents data product end to end design and development
Data annotation, labeling and other related data generation activities
Provides thought leadership for rest of team and seeks out opportunities to mentor more junior team members
Presents and holds data product updates and trainings Updates team on data product performance Education & Experience BS in Statistics, Mathematics, Computer Science or another quantitative field with at least 6 years experience manipulating data sets and building GLM/regression models, ensemble decision trees and neural networks. Graduate degree preferred.
Key Qualifications
Strong problem solving skills with an emphasis on product development.
6+ years of experience developing data science products
Strong experience using and optimizing common python machine and deep learning libraries such as Scikit learn, PyTorch, TensorFlow, Keras, MXNet and Spark MLlib
Experience using statistical computer languages (Python, R, Scala, SQL, etc.) to manipulate data and draw insights from large data sets.
Hands-on generative AI development Experience using foundation models (LLMs, VLMs)
Experience with model fine tuning of open source foundation models with proprietary data
Experience leveraging AI metrics for monitoring and value tracking
Knowledge of AI Agent frameworks with recent hands-on experience building an AI agent able to autonomously use data stores, tools and other AI models to solve inquiries.
Deep knowledge of data science concepts and related product development lifecycle
Experience using machine learning libraries such as TensorFlow, Keras, SparkML etc.
Working knowledge of machine learning tuning optimization procedures
Experience working with and creating data architectures.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master new technologies and techniques.
Preferred
● Experience with big data analytical frameworks such as Spark/PySpark
● Experience analyzing data from 3rd party providers: Google Knowledge Graph, Wikidata, etc.
● Experience visualizing/presenting data for stakeholders using: Looker, PowerBI, Tableau, etc.

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