Senior Data Scientist

Oorwin AI; St. Louis (MO); Remote; USD 100,000 - 130,000; Full time

Job summary

Oorwin AI is looking for a Senior Data Scientist specializing in Applied Machine Learning, focused on developing ML solutions for enhancing Cisco Learning engagements. The role requires deep ML expertise, particularly in traditional predictive ML and feature engineering.

As part of a two-person team, you will develop and implement ML workflows, understanding existing data signals to drive feature engineering processes.

Qualifications

• Deep ML expertise is mandatory.
• Hands-on experience with predictive ML and feature engineering.
• Experience in Sales-domain ML and predictive analytics is crucial.

Responsibilities

• Develop ML solutions for identifying high-value engagement opportunities.
• Understand existing signals and build experiments against data sources.
• Design and propose feature-engineering ML stages.

Skills

Statistical modeling

Classification modeling

Feature engineering

Feature importance analysis

Model evaluation

Logistic Regression

Advanced Python

SQL

NumPy

Tools

Scikit-learn

MLflow

Job description

Senior Data Scientist – Applied Machine Learning

Remote

3-6+ Months

Hands-on senior technical resource on a two-person KCS project team focused on developing an ML solution for identifying high-value Cisco Learning engagement opportunities.

Required Skills

Traditional predictive ML

• Statistical modeling
• Classification / probability-based modeling
• Feature engineering and feature selection
• Feature importance / ablation analysis
• Model evaluation and calibration
• Holdout and temporal validation
• Leakage identification and prevention
• Logistic Regression
• Scikit-learn or comparable ML framework

Traditional ML, feature engineering, statistical modeling, classification/probability analysis, class imbalance, and seasonality as key areas of need.

Languages / Data

Advanced Python

• SQL
• NumPy
• Relational/database analysis
• Structured + unstructured data

ML Lifecycle

ML training/evaluation workflows

• Experiment tracking
• Automated testing/retraining concepts
• Production-oriented ML practices

The operating model states that Kforce work will include understanding existing signals, building experiments against individual data sources, determining associated features/dimensions, and designing/proposing feature-engineering ML stages. Deep ML expertise is mandatory.

Preferred Skills

Sales-domain feature engineering / predictive analytics

• Propensity modeling / opportunity or lead scoring
• Revenue-oriented predictive analytics
• Customer adoption, consumption, or renewal modeling
• Survival / time-to-event analysis
• NLP / text analytics
• MLflow
• Feature Store concepts
• Enterprise data and governance

Important recruiter emphasis: Sales-domain ML/feature-engineering experience should be treated as especially important. Client Stakeholder explicitly wrote that“Experience and Expertise in ML, Feature Engineering in Sales Domain is must.”

Skills :