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Weekend Data Science Jobs in Schenectady, NY (NOW HIRING)

The data science center leads the development and implementation of machine learning and AI solutions that improve university operations and student outcomes. This role will focus on building ...

Required : • Master's degree in data science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field. • 2-3 years experience of data scientist or analyst ...

Sr Data Scientist

Albany, NY · On-site

$85K - $95K/yr

The data science center leads the development and implementation of machine learning and AI solutions that improve university operations and student outcomes. This role will focus on building ...

This role operates at the intersection of data science, data engineering, cloud analytics platforms, and business strategy, serving as a technical authority and thought leader across complex ...

This role operates at the intersection of data science, data engineering, cloud analytics platforms, and business strategy, serving as a technical authority and thought leader across complex, high ...

Five years of relevant experience in data science, analytics, pricing, actuarial, or related quantitative roles. A master's degree may be considered in lieu of one to two years of experience

Senior Data Scientist - Machine Learning Specialist We are seeking a talented and innovative Senior Data Scientist to join our growing analytics team. In this impactful role, you will leverage your ...

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Weekend Data Science information

See Schenectady, NY salary details

$36.3K

$118.8K

$190.1K

How much do weekend data science jobs pay per year?

As of Aug 5, 2026, the average yearly pay for weekend data science in Schenectady, NY is $118,753.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,300.00 and $131,600.00 per year, depending on experience, location, and employer.

Do weekend data scientists work on weekends?

Weekend data scientists may work on weekends if their projects or deadlines require it, but many roles follow a standard weekday schedule. Flexibility depends on the employer, project needs, and whether the position involves on-call or urgent tasks. Typically, data science roles are performed during regular business hours, but some positions may require weekend work for data collection, analysis, or reporting.

What skills and qualifications are needed to thrive as a weekend data scientist?

To thrive as a Weekend Data Scientist, you need strong analytical skills, proficiency in statistics, and expertise in programming languages such as Python or R, often supported by a degree in a quantitative field. Familiarity with data analysis tools like SQL, machine learning libraries (e.g., scikit-learn, TensorFlow), and data visualization platforms (e.g., Tableau) is typically required. Excellent time management, problem-solving ability, and effective communication are crucial soft skills for delivering insights on tight weekend deadlines. These skills ensure that data-driven decisions can be made efficiently and accurately, even within limited time frames.

What is a weekend data science?

A Weekend Data Science job typically refers to a part-time or contract-based data science position where the primary work hours are on weekends. These roles are ideal for students, professionals seeking extra income, or those looking to gain experience in the data science field without committing to a full-time weekday schedule. Weekend data scientists analyze data, build models, and generate insights just like full-time data scientists but with flexible or reduced hours that fit around a weekend schedule.

What challenges do data scientists working on weekends face, and how can they be managed?

Data scientists working weekend shifts often encounter challenges such as limited access to colleagues for collaboration or support, since many team members may not be available outside standard business hours. Additionally, urgent issues or data anomalies may require quick, independent problem-solving. Proactive communication with weekday teams, thorough documentation, and setting up clear protocols for handoffs can help manage these challenges and ensure smooth workflow continuity.

What is the difference between Weekend Data Science vs Part-Time Data Analyst?

AspectWeekend Data SciencePart-Time Data Analyst
CredentialsTypically requires a degree in data science, statistics, or related fieldOften requires a degree or relevant experience in data analysis or related fields
Work EnvironmentProject-based, flexible hours, often remote or on-site during weekendsFlexible hours, may be remote or on-site, often with less technical complexity
Industry UsageUsed in tech, finance, healthcare, and startups for specialized projectsCommon in retail, marketing, and small businesses for routine data tasks

Weekend Data Science roles focus on complex data projects requiring advanced skills, often during weekends, while Part-Time Data Analysts handle routine data tasks with less technical depth, offering flexible schedules. Both roles serve different needs but share a focus on data work outside standard hours.

What are the most commonly searched types of Data Science jobs in Schenectady, NY? The most popular types of Data Science jobs in Schenectady, NY are:
What are popular job titles related to Weekend Data Science jobs in Schenectady, NY? For Weekend Data Science jobs in Schenectady, NY, the most frequently searched job titles are:
What job categories do people searching Weekend Data Science jobs in Schenectady, NY look for? The top searched job categories for Weekend Data Science jobs in Schenectady, NY are:
What cities near Schenectady, NY are hiring for Weekend Data Science jobs? Cities near Schenectady, NY with the most Weekend Data Science job openings:

$85K - $95K/yr

Full-time

Re-posted 26 days ago


Job description

The data science center leads the development and implementation of machine learning and AI solutions that improve university operations and student outcomes. This role will focus on building, deploying, and scaling predictive and generative AI capabilities—including large language model (LLM) applications—to support strategic initiatives across marketing, admission, student advising, and academic affairs. The ideal candidate combines deep technical expertise with strong business partnership skills and a passion for applying cutting-edge AI technologies in a higher education environment.

Duties and Responsibilities: 

• Design, develop, validate, and deploy machine learning models to improve operational efficiency, decision-making, and student success outcomes.
• Build predictive models and intelligent decision-support tools for use cases such as enrollment marketing, student advising, course engagement, persistence, and retention.
• Develop and implement LLM-powered solutions by leveraging popular LLM APIs for university stakeholders.
• Evaluate emerging AI and machine learning technologies and recommend practical adoption strategies aligned with university goals, governance, and responsible AI principles.
• Partners with leaders and subject matter experts across marketing, advising, academic affairs, teaching and learning, and student success to identify high-impact opportunities for analytics and automation.
• Translate complex business problems into data science solutions, including experimentation, feature engineering, model development, and performance monitoring.
• Collaborate with IT teams to productionize models, integrate solutions into workflows, and maintain scalable, reliable data products.
• Communicate insights, model results, and recommendations clearly to technical and non-technical audiences through presentations, dashboards, and written documentation.

Qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

  • Master’s degree in data science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
  • 2-3 years experience of data scientist or analyst.
  •  Demonstrated experience developing and implementing machine learning models in production environments.
  • Strong proficiency in Python, SQL, and common machine learning frameworks and libraries.
  • Experience with end-to-end model lifecycle management, including data preparation, feature engineering, training, validation, deployment, and monitoring.
  • Hands-on experience with LLMs, natural language processing, prompt design, evaluation, and/or generative AI applications.
  • Familiarity with cloud platforms and modern data science tooling for scalable model development and deployment.
  • Strong analytical, problem-solving, and communication skills, with the ability to influence decisions through data-driven insights.
  • Ability to work cross-functionally and manage multiple priorities in a collaborative environment.

The hiring salary range for this position is $85,000 - $95,000. The hiring salary range above represents the University’s good faith estimate at the time of posting.