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Entry Level Data Scientist Machine Learning information

What are the key skills and qualifications needed to thrive as an Entry Level Data Scientist in Machine Learning, and why are they important?

To thrive as an Entry Level Data Scientist in Machine Learning, you need a solid background in statistics, programming (Python or R), and foundational machine learning concepts, typically supported by a relevant degree in computer science, data science, or a related field. Familiarity with tools and libraries such as scikit-learn, TensorFlow, Pandas, and SQL, as well as experience with data visualization platforms, is highly valuable. Strong problem-solving skills, attention to detail, and the ability to communicate technical findings clearly set candidates apart. These skills are essential for effectively analyzing data, building predictive models, and translating complex results into actionable business insights.

What are entry level data scientist machine learning jobs?

Entry level data scientist machine learning jobs are positions for individuals who are new to the field of data science and machine learning. These roles typically focus on working with data, building and testing machine learning models, and supporting more experienced data scientists. Entry level professionals may clean and analyze data, implement basic algorithms, and help interpret results to inform business decisions. These jobs often require proficiency in programming languages like Python or R, foundational knowledge of statistics, and some experience with machine learning libraries.

What are some common challenges faced by entry-level data scientists working with machine learning models?

Entry-level data scientists often encounter challenges such as cleaning and preparing messy or incomplete datasets, selecting appropriate algorithms for specific problems, and tuning model parameters to achieve optimal performance. In addition, they may need to clearly communicate technical findings to non-technical stakeholders and collaborate closely with team members from engineering, product, and business departments. Gaining experience in version control, reproducibility, and model deployment are also important steps in mastering the end-to-end machine learning workflow.
What are popular job titles related to Entry Level Data Scientist Machine Learning jobs in Connecticut? For Entry Level Data Scientist Machine Learning jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Entry Level Data Scientist Machine Learning jobs in Connecticut look for? The top searched job categories for Entry Level Data Scientist Machine Learning jobs in Connecticut are:
What cities in Connecticut are hiring for Entry Level Data Scientist Machine Learning jobs? Cities in Connecticut with the most Entry Level Data Scientist Machine Learning job openings:
Entry Level Data Scientist Machine Learning jobs near you

Lead Data Scientist (Artificial Intelligence/Machine Learning)

Criminal Investigation & Law Enforcement | IRS Careers

New Haven, CT • On-site

$125K/yr

Other

Posted 2 days ago

Job description

WHAT IS INFORMATION TECHNOLOGY ?
A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions
  • Position(s) are to be filled in following area(s):
    • IT - Taxpayer Services and Online Accounts
  • Consider each location carefully when applying. If you are selected for a location, that location will become your official post of duty.
REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILSQualifications:

Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the closing date of this announcement.
BASIC REQUIREMENTS All GRADES: EDUCATION:
You must have a bachelor's or higher degree in mathematics, statistics, computer science, data science or other field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.
OR
COMBINATION OF EDUCATION AND EXPERIENCE: A combination of education and experience that includes courses equivalent to a major field of study (30 semester hours) as shown in the paragraph above, plus additional education or appropriate experience.
SPECIALIZED EXPERIENCE GRADE 14: In addition to the basic requirements, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-13 grade level in the Federal service. Specialized experience for this position includes:

  • Designing, developing, integrating, testing, and supporting conversational AI solutions, virtual assistants, chatbots, digital messaging platforms, voice automation, interactive voice response (IVR) platforms, or generative AI-enabled customer engagement solutions in a production environment.
  • Developing and optimizing natural language understanding (NLU), natural language processing (NLP), speech recognition, intent classification, entity recognition, conversational workflows, or automated self-service solutions supporting customer interactions across voice and digital channels.
  • Designing, testing, implementing, and refining prompt engineering strategies, generative AI workflows, large language model (LLM) integrations, and AI-assisted customer engagement capabilities to improve automation, containment, customer experience, and operational outcomes.
  • Integrating conversational AI, generative AI, voice, chat, messaging, or digital engagement platforms with enterprise applications, APIs, backend systems, authentication services, customer data platforms, or knowledge management solutions.
  • Demonstrating subject matter expert (SME)-level proficiency in at least one modern programming language such as Java or Python, including development of backend services, automation, integrations, data processing pipelines, or conversational application logic.
  • Analyzing customer interaction data, conversation transcripts, chat sessions, operational metrics, and user behavior to identify trends, improve AI performance, evaluate model effectiveness, and enhance customer experience outcomes.
  • Developing, querying, and analyzing large datasets using cloud-based analytics platforms and data warehouses to support AI model evaluation, operational reporting, and business decision-making.
  • Troubleshooting and resolving complex system integration, application reliability, authentication, speech processing, conversational AI, generative AI, digital engagement, or performance issues across interconnected platforms.
  • Applying DevSecOps, CI/CD pipelines, automated testing, version control, and agile software development practices in enterprise environments.
  • Collaborating with business stakeholders, architects, engineers, cybersecurity personnel, data scientists, and operations teams to translate business requirements into AI-enabled technical solutions.

AND
You must also meet the following requirement(s):

  • PERFORMANCE RATING: Current federal employees must have at least a fully successful or equivalent performance rating to receive consideration.
  • TIME AFTER COMPETITIVE APPOINTMENT (TACA): By the closing date (or if this is an open continuous announcement, by the cut-off date) specified in this job announcement, current civilian employees must have completed at least 90 days of federal civilian service since their latest non-temporary appointment from a competitive referral certificate, known as time after competitive appointment. For this requirement, a competitive appointment is one where you applied to and were appointed from an announcement open to "All US Citizens"
  • TIME IN GRADE (TIG): Federal employees must meet time-in-grade requirements. For positions above the GS-05,applicants must meet applicable time-in-grade requirements to be considered eligible. One year (52 weeks) at the next lower grade level is required to meet the time-in-grade requirements for the grade you are applying for. For positions at the GS-05, you cannot advance to the GS-05 if you have held a GS-02 in the past 52 weeks. There is no TIG restriction for GS-02, 03, or 04 positions.


For more information on qualifications please refer to OPM's Qualifications Standards.

Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER

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