{"id":2439304,"url":"https://alion.io/job/freshtalent-junior-data-scientist","title":"Junior Data Scientist","company":{"id":100173,"name":"FreshTalent","domain":"freshtalent.africa","url":"https://alion.io/company/freshtalent","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DZ"],"hiring_countries_total":1,"salary":{"min":288000,"max":384000,"currency":"USD","period":"year","gross":null,"usd_annual":384000},"salary_estimate":null,"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-13T11:19:28Z","employer_posted_date":null,"last_verified_at":"2026-10-13T11:19:28Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Junior Data Scientist - AI Model Evaluation\nCompensation\nAnnualized Compensation: $288,000-$384,000 per year\nHourly Rate: $150-$200 per hour\nEmployment Type: Contractor\nWork Arrangement: Remote\nThe annualized figure is based on 40 hours per week and 48 working weeks per year. Actual earnings will depend on project workload, assignments, and hours worked.\nAbout the Role\nWe are seeking data scientists and quantitative professionals with experience at leading organizations to evaluate and improve how AI models handle statistics, machine learning, experimentation, and quantitative reasoning.\nIn this role, you will assess AI-generated responses to data science and quantitative problems, develop expert-level prompts and reference solutions, and identify methodological weaknesses, statistical errors, and flawed reasoning.\nYour expertise will help strengthen AI model performance by ensuring that outputs demonstrate sound analytical methods, statistical rigor, and reliable quantitative reasoning.\nKey Responsibilities\nEvaluate AI model outputs across data science, statistics, machine learning, and quantitative problem-solving tasks.\n\nCreate expert-level prompts that test analytical ability, statistical understanding, and machine learning knowledge.\n\nDevelop datasets and reference solutions for evaluating model performance.\n\nIdentify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.\n\nAssess the accuracy, validity, and rigor of AI-generated analytical solutions.\n\nProvide structured feedback highlighting errors, limitations, and opportunities for improvement.\n\nCommunicate technical findings and statistical concepts clearly to support model development.\n\nContribute to improving AI model capabilities in data science and quantitative analysis.\n\nRequired Qualifications\nAt least one year of professional experience at a leading company in technology, finance, or research, with at least part of this experience gained within the past three years.\n\nStrong understanding of statistics, machine learning, and quantitative reasoning.\n\nProficiency in Python for data analysis and quantitative problem-solving.\n\nFamiliarity with data analysis, experimentation, and data modeling.\n\nKnowledge of relevant data science tools and libraries, including pandas.\n\nAbility to identify statistical errors, evaluate methodologies, and assess analytical conclusions.\n\nStrong written communication skills, including the ability to explain technical and statistical concepts clearly.\n\nBased in an English-speaking country.\n\nPreferred Qualifications\nExperience evaluating statistical analyses, machine learning approaches, or quantitative solutions.\n\nExperience designing experiments and interpreting analytical results.\n\nFamiliarity with developing datasets, technical prompts, and reference solutions.\n\nExperience identifying methodological weaknesses and validating quantitative conclusions.\n\nAbility to provide structured, actionable feedback on complex data science problems.\n\nKey Skills\nData Science\n\nPython\n\nMachine Learning\n\nStatistics\n\nData Analysis\n\nExperimentation\n\nPandas\n\nData Modeling\n\nQuantitative Reasoning\n\nStatistical Analysis\n\nMethodology Evaluation\n\nModel Output Evaluation\n\nPrompt Development\n\nDataset Development\n\nReference Solution Development\n\nAnalytical Problem Solving\n\nCritical Thinking\n\nTechnical Writing\n\nStatistical Communication\n\nWhat Success Looks Like\nAI-generated solutions are evaluated for statistical accuracy, methodological soundness, and quantitative rigor.\n\nExpert-level prompts, datasets, and reference solutions effectively assess data science capabilities.\n\nStatistical errors and weaknesses in quantitative reasoning are identified and clearly documented.\n\nFeedback provides actionable guidance for improving AI model performance.\n\nEvaluations contribute to stronger AI capabilities in statistics, machine learning, experimentation, and data 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