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Weekend Data Science R Jobs in Indiana (NOW HIRING)

Keystone

ML Engineer

Indianapolis, IN

Strong expertise in data analysis tools and languages (e.g., Python, R, SQL). * Experience with ... Bachelor's degree in Data Science, Statistics, Computer Science, or related field. Master's degree ...

Keystone Cooperative

ML Engineer

Indianapolis, IN · On-site

Strong expertise in data analysis tools and languages (e.g., Python, R, SQL). * Experience with ... Bachelor's degree in Data Science, Statistics, Computer Science, or related field. Master's degree ...

INCOG

QC Scientist (Weekend Shift)

Fishers, IN

Weekend QC Scientist Summary The Weekend QC Scientist supports GMP manufacturing and laboratory ... Perform second-person verifications or peer reviews of data and documentation, as trained and ...

Lilly

Sr. Scientist - TS/MS Digital Plant

Lebanon, IN

$87.60K - $119.70K/yr

... Science (TS/MS) team and provides technical leadership and expertise in the development ... Proficiency in data analysis, visualization, and statistical tools (e.g., Python, R, SIMCA, JMP)

MISO

Principal AI Engineer

Carmel, IN · On-site

$168K - $193K/yr

Partnering with data scientists, engineers, and technology teams to integrate AI into real-time and ... R and Python (required) Appropriate level will be determined based upon experience and knowledge.

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Weekend Data Science R information

What are the key skills and qualifications needed to thrive as a Weekend Data Science R, and why are they important?

To thrive as a Weekend Data Science R, you need a solid background in statistics, programming (especially in R), and data analysis, often supported by a relevant degree or coursework. Familiarity with data visualization tools, machine learning libraries, and version control systems like Git is commonly required. Excellent problem-solving, time management, and communication skills help you tackle projects independently and convey insights clearly. These skills are crucial for delivering actionable results efficiently while balancing part-time or weekend schedules.

What are some common challenges faced by Weekend Data Science R professionals, and how can they effectively manage project deadlines given a limited work schedule?

Weekend Data Science R professionals often face the challenge of managing complex data analysis projects within a restricted timeframe. Balancing project deadlines with limited availability requires strong time management and clear communication with team members. It's important to set realistic goals for each work session, prioritize tasks that drive the most value, and leverage collaboration tools to stay aligned with colleagues working during weekdays. Building a habit of thorough documentation and regularly syncing with the team ensures that progress continues smoothly, even when not physically present during the standard workweek.

What is a Weekend Data Science R?

A Weekend Data Science R is typically a data scientist or analyst who specializes in using the R programming language to analyze and interpret data, and who works primarily on weekends. This role may be part-time, project-based, or designed for individuals who are balancing other commitments during the week. Weekend Data Science R professionals often handle tasks such as data cleaning, statistical analysis, and creating data visualizations using R. They are valued for their ability to deliver insights and support decision-making processes, often on a flexible schedule. This role is ideal for those who have strong analytical skills and proficiency in R, and who prefer or require weekend work hours.
What cities in Indiana are hiring for Weekend Data Science R jobs? Cities in Indiana with the most Weekend Data Science R job openings:
Weekend Data Science R jobs near you
ML Engineer

ML Engineer

Keystone

Indianapolis, IN

Full-time

Posted 12 days ago

Job description

Job Description

Overview: Our company is seeking a detail-oriented and highly analytical ML Engineer who will assist in driving our AI-driven product development initiatives. The successful candidate will possess a strong understanding of data analysis, along with the ability to apply AI and ML methodologies to enhance our products.

Must be authorized to work in the United States now and in the future, without company sponsorship. Must be able to work onsite in the corporate office in Indianapolis, Indiana. (This role is not hybrid or remote).

Duties and Responsibilities:

Include but are not limited to:

  • Collaborate with cross-functional teams to understand business requirements and develop data-driven solutions tailored to those needs.
  • Utilize Snowflake Cortex and other AI/ML development tools to design, implement, and optimize data models and algorithms.
  • Conduct exploratory data analysis to uncover trends, patterns, and insights that can inform business decisions.
  • Analyze complex datasets to extract actionable insights that inform product development strategies.
  • Conduct complex statistical analyses, including regression modeling, hypothesis testing, and predictive analytics, to forecast trends and inform strategic decisions.
  • Assist in developing machine learning models and algorithms to solve complex business problems and improve operational efficiency.
  • Create and maintain detailed documentation of data analysis processes and AI/ML model development.
  • Monitor and evaluate the effectiveness of AI/ML models in production and refine them as needed.
  • Provide training and support to team members and stakeholders on data analysis tools and techniques.

Skills and Qualifications:

  • Proven experience as a Data Analyst or Data Engineer, preferably with experience incorporating AI/ML methodologies.
  • Strong expertise in data analysis tools and languages (e.g., Python, R, SQL).
  • Experience with machine learning frameworks and tools (e.g., TensorFlow, PyTorch).
  • Excellent problem-solving skills and attention to detail.
  • Ability to communicate complex findings in a clear and concise manner.
  • Strong teamwork and collaboration skills.

Education and Experience:

  • Bachelor's degree in Data Science, Statistics, Computer Science, or related field. Master's degree preferred.

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