Back to jobs

Senior Data Analyst (Remote | $50–$70/hr)

SynthiresAnywherePosted 2d ago
Data Analyst
Craft my tailored resume free

Free to start · No card

Job description

Senior Data Science Specialist Position: Senior Data Science Specialist Type: Hourly Contract (Part-Time) Compensation: $50–$70/hour Location: Remote About the Opportunity This opportunity is for experienced Senior Data Science Specialists interested in contributing to advanced AI research and evaluation projects focused on improving the accuracy, reasoning, and analytical capabilities of next-generation AI systems. The role involves evaluating AI-generated data science solutions, assessing statistical and machine learning methodologies, and applying real-world data science expertise to improve the performance of advanced AI models. No prior AI experience is required. Your data science expertise, analytical skills, and professional experience are the primary qualifications for success in this role. Responsibilities Evaluate AI-generated data science solutions, statistical analyses, and machine learning outputs for accuracy and quality.Solve complex data analysis, statistical modeling, machine learning, and predictive analytics problems.Review AI-generated approaches involving data preprocessing, feature engineering, model development, validation, and evaluation.Analyze datasets, statistical methodologies, predictive models, and analytical workflows.Identify errors, inconsistencies, methodological issues, and edge cases in AI-generated analyses.Compare multiple AI-generated solutions and assess their technical strengths, weaknesses, and reasoning quality.Provide structured technical feedback to improve AI accuracy, analytical reasoning, and model performance.Collaborate with technical reviewers and interdisciplinary teams to maintain consistent evaluation standards and data quality.Required Qualifications 2+ years of professional Data Science experience or equivalent industry/research experience.Strong experience with data analysis, statistical modeling, machine learning, predictive modeling, and data visualization.Proficiency in Python, SQL, and common data science libraries and frameworks.Strong understanding of statistics, probability, machine learning algorithms, model evaluation, and experimental design.Experience working with real-world datasets and translating data into actionable insights.Ability to evaluate AI-generated analyses, models, and technical solutions for correctness and effectiveness.Excellent analytical, problem-solving, and written communication skills.Preferred Qualifications Experience working with large-scale datasets, advanced machine learning systems, or production data science workflows.Experience with PyTorch, TensorFlow, scikit-learn, XGBoost, or similar machine learning frameworks.Familiarity with AI, generative AI, LLMs, AI model evaluation, or data annotation.Experience with A/B testing, experimentation, causal inference, time-series analysis, or advanced statistical methods.Familiarity with cloud platforms, distributed computing, MLOps, or data engineering workflows.Prior experience with AI evaluation, technical review, or model quality assessment projects.Compensation Competitive compensation of $50–$70/hour.Weekly payments.Independent contractor engagement.Application Process Easy Apply on LinkedInCheck Email for Next StepsParticipate in Resume Evaluation & Interview Stage

Prepare your application

Use the description above to assess your fit for Synthires. This checklist is guidance from Resumize, rather than additional requirements from the employer. Imported listings can be shortened or change after collection, so confirm the complete description and current availability before sending an application.

Match requirements to evidence

Identify the responsibilities and skills the employer explicitly names. For each important requirement, choose one example from your work, education, training, or projects. Explain what you did, how you did it, and what changed as a result. Include a measurable outcome when you can support it. If you lack a requirement, describe related experience accurately instead of adding an unfamiliar skill to your resume.

Put the most relevant examples near the top of your resume. Keep job titles, dates, qualifications, and contact details consistent across your application materials. Use the employer’s terminology when it describes your experience accurately; a copied list of keywords does not explain your contribution. A short, specific bullet is easier to review than a paragraph that mixes several unrelated duties.

Check the working arrangement

Confirm the location, schedule, employment type, and any eligibility requirements on the employer’s site. A remote label does not establish worldwide eligibility or flexible hours. If compensation, benefits, equipment, travel, or contract duration are missing from this listing, write down your questions for the recruiter. Do not assume details from another opening at the same company apply to this role.

Review before submitting

Follow the employer’s requested file format and application instructions. Open your exported resume and check that its text is selectable, its sections read in order, and its links work. Proofread names, dates, and contact information. Share confidential work samples only when you have permission, and use an anonymized example when necessary.

Submit through a trusted employer or recruiting destination. If the original listing has disappeared, search the employer’s careers page for the title rather than assuming the vacancy remains open. Save the application date, job reference, and the version of your resume you sent. Those details help you prepare for a later conversation and avoid submitting conflicting information through several job boards.

Review your resume for writing and readability feedback, or browse other openings if this opportunity is unavailable.