Data Scientist Lead

Lancesoft; Oshkosh (WI); On-site; USD 130,000 - 160,000; Full time

Job summary

Lancesoft is seeking a seasoned ML Engineer to lead end-to-end ML initiatives on-site in Oshkosh, WI or New Hudson, MI. You will own projects from data extraction to deployment, collaborating with business stakeholders and delivering robust NLP/LLM solutions using modern cloud ecosystems.

Requirements include 5+ years in ML, strong Python/SQL, and hands-on experience with distributed compute and BI tools. This role emphasizes delivering business impact with scalable architectures and measurable

Qualifications

• 5+ years of experience in ML engineering or data science.
• Experience deploying end-to-end ML solutions with business stakeholders.
• Strong programming in Python and SQL.

Responsibilities

• Own end-to-end ML solution delivery from data to deployment.
• Collaborate with business stakeholders to translate needs into solutions.
• Demonstrate model performance and present insights to stakeholders.

Skills

Python ML

SQL

NLP/LLM

Cloud platforms

Distributed compute

ML tooling

BI visualization

Tools

Docker

Kubernetes

MLflow

Airflow

Kubeflow

Databricks

Snowflake

Apache Spark

Power BI

Streamlit

Job description

Salary Range: $130000 to $160000 Annually

Onsite in Oshkosh, WI or New Hudson, MI

Core requirements

• 5+ years of experience; 6 years may be appropriate, according to the manager.
• AI-savvy and business-savvy, with strong business relationships and experience working directly with business stakeholders—not solely back-end development.
• Ability to manage work end to end and personally deploy and deliver the solution.

Technical Expertise

• Expert proficiency in Python (including NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow) and advanced SQL.
• Strong hands-on experience building supervised/unsupervised learning pipelines, deep learning models, natural language processing (NLP), and modern Large Language Model (LLM) architectures.
• Proven track record utilizing enterprise cloud ecosystems (AWS, Azure, or GCP) and distributed compute systems (Databricks, Snowflake, Apache Spark).
• Demonstrated familiarity with model tracking, containerization, and orchestration platforms (e.g., MLflow, Docker, Kubernetes, Kubeflow, Airflow).
• Proficiency in presenting findings via business intelligence and visualization tools (e.g., Power BI, Streamlit).