Tech Lead

Keylent, Inc.

$100K — $130K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Statistics, Applied Mathematics or related field
  • 5+ years of experience in data analytics, data mining, and statistical analysis
  • Strong pattern recognition skills
  • Proficient in predictive modeling techniques
  • Expertise in implementing various machine learning algorithms like logistic regression, decision trees, and neural networks.

Responsibilities

  • Assist in expanding capabilities in Positive Train Control analytics
  • Collect and analyze large data sets
  • Interpret complex data and provide actionable insights
  • Work independently on major analytical projects
  • Utilize statistical and programming knowledge for data interpretation.

Benefits

  • Opportunity to work on innovative projects in a growing field
  • Collaborative work environment
  • Flexible work arrangements
  • Professional development opportunities
  • Access to advanced analytics tools and technologies.
Full Job Description
Visa status: U.S. Citizens and those authorized to work in the U.S. are encouraged to apply.
Tax Terms: W2, 1099
Corp-Corp or 3rd Parties: Yes

Description
Data Scientist

What You Will Be Doing:
The Data scientist will assist in our efforts to expand our capabilities within Positive Train Control analytics. This role will use analytical, statistical, and programming knowledge to collect, analyze and interpret large data sets. Experienced candidate needs to be innovative and show proven ability to work independently on big projects.

Must Haves:
• Bachelor's degree from an accredited university in Computer Science, Statistics, Applied Mathematics or related field
• 5 years+ experience in data analytics, data mining and statistical analysis
• Excellent pattern recognition and predictive modeling skills
• Must have a clear understanding and implementation of different machine learning algorithms such as logistic regression, decision trees, SVM, Naïve Bayes, KNN, neural networks, gradient descent, Random forest, etc

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