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Weekend Data Science Jobs in New Mexico (NOW HIRING)

Data Analyst

Moriarty, NM ยท On-site

$85K - $110K/yr

Bachelor's degree in Data Science, Statistics, Mathematics, Engineering, Computer Science, or related field, or equivalent experience. * 2-4 years of experience in a data analyst or similar ...

Data Engineer

Albuquerque, NM ยท On-site

$100 - $125/hr

Create data tools for analytics and data science teams to build and optimize data products. * Grow your communication and technical skills by merging consulting and big data to create dataโ€‘centric ...

$72K - $91K/yr

The role emphasizes experimentation, rapid prototyping, and close collaboration with data engineers, data scientists, and business stakeholders. Responsibilities * Document and evaluate business ...

Data Engineer

Albuquerque, NM ยท On-site

$111K - $133K/yr

Here, you'll work with a multi-disciplinary team of data analysts, engineers, scientists, electrical engineers, test engineers, software developers, and data consumers in a fast-paced, agile ...

Data Engineer

Albuquerque, NM ยท On-site +1

$111K - $133K/yr

Here, you'll work with a multi-disciplinary team of data analysts, engineers, scientists, electrical engineers, test engineers, software developers, and data consumers in a fast-paced, agile ...

Data Engineer

Albuquerque, NM ยท On-site

$111K - $133K/yr

Here, you'll work with a multi-disciplinary team of data analysts, engineers, scientists, electrical engineers, test engineers, software developers, and data consumers in a fast-paced, agile ...

$96K - $116K/yr

Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver high-quality datasets. * Implement data quality checks, monitoring, and alerting ...

Showing results 21-40

Weekend Data Science information

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 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 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.

Do weekend data scientists work on weekends?

Weekend data scientists may work on weekends depending on project deadlines, company policies, or client needs. Typically, data science roles involve regular weekday hours, but some positions require weekend work, especially in roles with flexible or project-based schedules. It is important to clarify work hours during the hiring process or in job descriptions.

What are the most commonly searched types of Data Science jobs in New Mexico?

The most popular types of Data Science jobs in New Mexico are:

What are popular job titles related to Weekend Data Science jobs in New Mexico?

For Weekend Data Science jobs in New Mexico, the most frequently searched job titles are:

What cities in New Mexico are hiring for Weekend Data Science jobs?

Cities in New Mexico with the most Weekend Data Science job openings:

Senior Data Scientist - Big Data R&D, Identity Graph & KYC (Miami)

Socure

Miami, NM โ€ข On-site

Full-time

Re-posted 6 days ago


Key responsibilities

  • Design, develop, and evaluate machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on large datasets.

  • Architect and optimize graph-based identity representations to improve match rates and support downstream fraud and KYC models.

  • Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation in AWS/Databricks environments.


Job description

Socure is building the identity trust infrastructure for the digital economy โ€” verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this wonโ€™t be your place. If you want to help build the future of identity with a team that holds a high bar for itself, keep reading.

About The Role

The Big Data R&D team develops cuttingโ€‘edge big data and graphโ€‘based solutions for entity search, entity resolution, and identity matching that power Socureโ€™s KYC and compliance products. As a Senior Data Scientist I, you will lead the design and deployment of advanced ML and graph algorithms on largeโ€‘scale PII datasets, own endโ€‘toโ€‘end projects from problem definition through production validation, and serve as a key technical partner to Product, Engineering, and Clientโ€‘facing teams. You will help define standards for feature engineering, experimentation, and data quality across our identity graph stack, with substantial impact on coverage, accuracy, and fairness.

What Youโ€™ll Do
  • Own the design, development, and evaluation of machine learning, statistical, and graphโ€‘based algorithms for entityโ€‘resolution, identity trust scoring, and anomaly detection on massive datasets.
  • Architect and optimize graphโ€‘based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models.
  • Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments.
  • Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout.
  • Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration.
  • Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps.
  • Provide deep analytical support to Socureโ€™s compliance and regulatory product suite, including investigative analyses, rootโ€‘cause analysis for anomalies, and clear narratives for internal and external stakeholders.
  • Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders.
  • Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality.
  • Communicate complex technical concepts and tradeโ€‘offs in a concise, structured way to both technical and nonโ€‘technical audiences (e.g., product reviews, customer meetings, internal briefings).
What You Bring
  • Masterโ€™s degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles; background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred.
  • Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikitโ€‘learn, XGBoost, TensorFlow or PyTorch.
  • Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets.
  • Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification).
  • Experience developing productionโ€‘quality data pipelines and automated workflows using Airflow or similar orchestration tools.
  • Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred.
  • Solid SQL skills and experience working with largeโ€‘scale analytical data stores.
  • Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent highโ€‘stakes domains is a plus.
  • Demonstrated ability to lead mediumโ€‘toโ€‘large projects endโ€‘toโ€‘end, make sound tradeโ€‘off decisions under ambiguity, and influence crossโ€‘functional stakeholders with data and clear reasoning.

Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring processโ€”including interview or onboarding supportโ€”please reach out to your Socure recruiting partner directly.

Compensation Range: $170K - $200K

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