Data Scientist: ML & Predictive Analytics

V15P1Talonnn; Chicago (IL); On-site; USD 123,000 - 232,000; Full time

Job summary

V15P1Talonnn in Chicago, IL is seeking a Data Scientist to uncover insights from large data sets and guide product decisions. You will apply data mining, statistical analysis, and predictive modeling to deliver high-quality analytics that inform product strategy.

The role emphasizes feature selection, model development with machine learning, data integration from third-party sources, data cleansing, and building anomaly detection systems with ongoing performance monitoring.

Qualifications

• Select features and build classifiers using machine learning techniques.
• Extend data with third party sources when needed.
• Enhance data collection procedures to include information relevant for analytic systems.
• Process, cleanse, and verify the integrity of data used for analysis.
• Do ad-hoc analysis and present results clearly.
• Create automated anomaly detection systems and monitor performance.

Responsibilities

• Selecting features, building and optimizing classifiers using machine learning techniques
• Extending company's data with third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Creating automated anomaly detection systems and constant tracking of its performance

Skills

Feature selection

Machine learning

Data mining

Statistical analysis

Job description

V15P1Talonnn in Chicago, IL is seeking a Data Scientist to uncover insights from large data sets and guide product decisions. You will apply data mining, statistical analysis, and predictive modeling to deliver high-quality analytics that inform product strategy.

The role emphasizes feature selection, model development with machine learning, data integration from third-party sources, data cleansing, and building anomaly detection systems with ongoing performance monitoring.