Data Science Expert
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Salary
$380k – $480k per year
Location
Remote (United States, China, India, Germany, Japan, France, Russia, United Arab Emirates, United Kingdom, Argentina, Australia, Austria, Belarus, Belgium, Bosnia and Herzegovina, Brazil, Bulgaria, Canada, Colombia, Croatia, Cyprus, Czech Republic, Denmark, Egypt, Finland, Hungary, Indonesia, Ireland, Israel, Italy, Kazakhstan, Kenya, Kyrgyzstan, Malaysia, Mexico, Montenegro, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Philippines, Poland, Romania, Saudi Arabia, Serbia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, Ukraine, Uzbekistan, Vietnam, Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Anguilla, Armenia, Aruba, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bermuda, Bhutan, Bolivia, Botswana, British Virgin Islands, Brunei Darussalam, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Cayman Islands, Chad, Chile, Central African Republic, Comoros, Cook Islands, Costa Rica, Cuba, Curaçao, Djibouti, Dominica, Dominican Republic, East Timor, Ecuador, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Falkland Islands, Faroe Islands, Fiji, French Guiana, French Polynesia, Gabon, Gambia, Georgia, Ghana, Greece, Greenland, Grenada, Guadeloupe, Guam, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Iceland, Iran, Iraq, Isle of Man, Jamaica, Jordan, Kiribati, Kosovo, Kuwait, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Maldives, Mali, Malta, Marshall Islands, Martinique, Mauritania, Mauritius, Mayotte, Micronesia, Moldova, Monaco, Mongolia, Montserrat, Morocco, Mozambique, Namibia, Nauru, Nepal, New Caledonia, Nicaragua, Niger, Niue, Norfolk Island, North Korea, North Macedonia, Northern Mariana Islands, Oman, Palau, Panama, Papua New Guinea, Paraguay, Peru, Pitcairn Islands, Portugal, Puerto Rico, Qatar, Réunion, Rwanda, Samoa, San Marino, Senegal, Seychelles, Sierra Leone, Sint Maarten, Slovakia, Slovenia, Solomon Islands, Somalia, South Sudan, Sri Lanka, Sudan, Suriname, Syria, Tajikistan, Tanzania, Togo, Tokelau, Tonga, Tunisia, Turkmenistan, Tuvalu, Uganda, Uruguay, Vanuatu, Venezuela, Western Sahara, Yemen, Zambia, Zimbabwe, Singapore, Gibraltar)
Seniority
Senior · 7+ years exp
Employment
Contractor
Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 4, 2026.
Overview
Company
Impact
Profile match
Alpheva AI provides expert LLM training data, human feedback, model evaluations, and agent environments grounded in real professional workflows.
Bring senior data science expertise to AI evaluation projects focused on statistical analysis, data quality, experiment design, and reproducibility.
Review model responses and worked examples for incorrect technical assumptions, missing failure cases, and reliability or security gaps. Assess whether the reasoning meets professional standards for the stated task.
Write reference answers and scoring criteria grounded in statistical analysis, data quality, experiment design, and reproducibility. Explain consequential errors, evidence gaps, and the reasoning behind your corrections.
This is remote, project-based contract work. Scope, hours, and assignments depend on project matching; the listed hourly range applies to matched work.
Responsibilities
- Evaluate advanced domain tasks and AI-generated responses for factual accuracy and professional judgment.
- Apply current standards, methods, and risk considerations from technology & computing.
- Document errors, edge cases, and the reasoning behind an ideal response.
- Collaborate asynchronously with Alpheva's research and evaluation teams.
Required Qualifications
- 7+ years of substantive experience in data science or applied statistics.
- Demonstrated senior-level judgment in statistical analysis, data quality, experiment design, and reproducibility, with substantial responsibility for reviewing or delivering this work.
- Ability to write precise reference answers, assess competing approaches, and justify corrections with traceable evidence.
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