Fraud AI / Data Senior Consultant (remote)
Guidehouse · New York, United States · 4 wk ago
On-siteInformation Technology$113k–$188k/yrFull-time
We need to output only HTML fragment, with headings for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Remove boilerplate.
We need to parse the job posting.
Key parts:
- Job Family Technology Consulting Travel Required Up to 50% Clearance Required Ability to Obtain Public Trust (these are metadata; we can fold into short p maybe.)
- "What You Will Do" paragraph: The AI/Data Senior Consultant leads discrete workstreams and delivers advanced AI and data solutions. This role combines technical depth, client engagement, and mentorship of junior team members while driving real‑world AI adoption.
- "Key Responsibilities" bullet list (multiple lines). We'll put under
for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Remove boilerplate.
We need to parse the job posting.
Key parts:
- Job Family Technology Consulting Travel Required Up to 50% Clearance Required Ability to Obtain Public Trust (these are metadata; we can fold into short p maybe.)
- "What You Will Do" paragraph: The AI/Data Senior Consultant leads discrete workstreams and delivers advanced AI and data solutions. This role combines technical depth, client engagement, and mentorship of junior team members while driving real‑world AI adoption.
- "Key Responsibilities" bullet list (multiple lines). We'll put under Responsibilities
with - each.
- "What You Will Need" includes requirements: Must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding with Guidehouse. Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred. Bachelor’s degree from an accredited college/university FIVE (5) or more years of experience in AI, data science, analytics, or engineering THREE (3) or more years of experience in a fraud/payment integrity-related role Strong development expertise in Python/R, SQL, and data modeling Strong statistical and data analytics expertise Proficiency working with various data structure formats (CSV/TXT, JSON, XML, Parquet, etc.) Experience developing, testing, and validating machine learning and other AI models at scale Experience with data visualization tools (PowerBI, Tableau, Looker, etc.) Experience with applying data analytics and artificial intelligence in the financial crime compliance and/or fraud, waste, and abuse prevention space Travel will be 25-50% for candidates not located in the DMV or NYC area
- "What Would Be Nice To Have" list: Experience building fraud and/or fincrime monitoring rules in one or more programming languages Proven ability to communicate complex concepts to non‑technical audiences Experience working for or supporting healthcare technology, healthcare business or healthcare process initiatives Cloud experience (AWS, Azure, GCP) including data and ML services Experience deploying models into production (MLOps) Experience with AI platforms (Databricks, Palantir, Vertex AI, SageMaker) Prior consulting or client‑facing delivery experience Federal or State Medicaid experience (directly as an employee or in a consulting role), particularly experience with fraud prevention and detection and/or program integrity experience with Medicaid
- Salary: The annual salary range for this position is $113,000.00-$188,000.00. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs.
- Benefits: Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace. Benefits Include Medical, Rx, Dental & Vision Insurance Personal and Family Sick Time & Company Paid Holidays Position may be eligible for a discretionary variable incentive bonus Parental Leave and Adoption Assistance 401(k) Retirement Plan Basic Life & Supplemental Life Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts Short-Term & Long-Term Disability Student Loan PayDown Tuition Reimbursement, Personal Development & Learning Opportunities Skills Development & Certifications Employee Referral Program Corporate Sponsored Events & Community Outreach Emergency Back-Up Childcare Program Mobility Stipend
- About Guidehouse: EEO etc. We should remove boilerplate (EEO, legal, etc). The "About Guidehouse" paragraph includes equal opportunity etc. According to rules, remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. So we should omit that entire "About Guidehouse" block.
We need sections: About the role (maybe from "What You Will Do"? Actually "What You Will Do" is description of role. Could map to "About the role". We'll put that as
About the role
with .
Responsibilities: from Key Responsibilities.
Requirements: maybe combine "What You Will Need" as Requirements? But we also have Qualifications, Skills sections. The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to decide what content goes where. We have:
- About the role: description.
- Responsibilities: list.
- Requirements: maybe the mandatory qualifications (degree, years experience, public trust, etc.)
- Qualifications: maybe the nice-to-have? Or could be separate.
- Skills: maybe technical skills (Python/R, SQL, data modeling, etc.) but we already have some in requirements. Could separate.
But we must not create sections with filler/placeholder text. Must have real specific content.
We can structure as:
About the role
...
Responsibilities
...
Requirements
...
(mandatory)
Qualifications
...
(nice-to-have)
Skills
Maybe we could list technical skills like Python/R, SQL, data modeling, statistical analytics, data formats, ML model dev, visualization tools, etc. But those are already in requirements. Could combine.
Alternatively, we could treat "What You Will Need" as Requirements and Qualifications combined, and then have Skills separate for technical proficiencies.
But we must avoid duplication. Let's think: The posting includes:
- Must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST"; ... (clearance)
- Bachelor’s degree
- FIVE (5) or more years of experience in AI, data science, analytics, or engineering
- THREE (3) or more years of experience in a fraud/payment integrity-related role
- Strong development expertise in Python/R, SQL, and data modeling
- Strong statistical and data analytics expertise
- Proficiency working with various data structure formats (CSV/TXT, JSON, XML, Parquet, etc.)
- Experience developing, testing, and validating machine learning and other AI models at scale
- Experience with data visualization tools (PowerBI, Tableau, Looker, etc.)
- Experience with applying data analytics and artificial intelligence in the financial crime compliance and/or fraud, waste, and abuse prevention space
- Travel will be 25-50% for candidates not located in the DMV or NYC area
These are all mandatory requirements.
Nice-to-have list:
- Experience building fraud and/or fincrime monitoring rules in one or more programming languages
- Proven ability to communicate complex concepts to non‑technical audiences
- Experience working for or supporting healthcare technology, healthcare business or healthcare process initiatives
- Cloud experience (AWS, Azure, GCP) including data and ML services
- Experience deploying models into production (MLOps)
- Experience with AI platforms (Databricks, Palantir, Vertex AI, SageMaker)
- Prior consulting or client‑facing delivery experience
- Federal or State Medicaid experience (directly as an employee or in a consulting role), particularly experience with fraud prevention and detection and/or program integrity experience with Medicaid
We could put these under Qualifications.
Skills: maybe we could list technical skills like Python/R, SQL, data modeling, statistical analytics, data formats, ML model dev, visualization tools. But those are already in requirements; we could still list them under Skills but that would duplicate content. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. Duplication is okay? It says preserve ALL substantive information; does not forbid duplication but better to avoid redundancy. However we need to follow the headings list; we could put skills under Skills heading, but then we would need to move those items from Requirements to Skills, leaving Requirements maybe just degree, experience, public trust, travel. But that would be splitting the original list; still preserving all info.
Let's decide: We'll create:
- About the role: description.
- Responsibilities: list.
- Requirements: mandatory items: Public Trust clearance, Bachelor's degree, 5+ years AI/data science, 3+ years fraud/payment integrity, Travel 25-50% (if not DMV/NYC). Possibly also include "Must be able to OBTAIN and MAINTAIN a Federal or DHS 'PUBLIC TRUST'..." etc.
- Qualifications: nice-to-have list.
- Skills: technical skills: Python/R, SQL, data modeling, statistical and data analytics expertise, proficiency with data formats (CSV/TXT, JSON, XML, Parquet, etc.), experience developing/testing/validating ML models at scale, experience with data visualization tools (PowerBI, Tableau, Looker, etc.), experience applying data analytics/AI in financial crime compliance/fraud waste abuse prevention.
- Pay: salary range.
- Benefits: list of benefits.
- Schedule: travel %? Actually schedule could be travel requirement. But we already have travel in Requirements. However we could put travel under Schedule heading. The instruction says include a section ONLY when posting has real specific content for it. Travel is mentioned: "Travel will be 25-50% for candidates not located in the DMV or NYC area". That's specific. So we could put that under Schedule.
Thus we need to avoid duplication: travel appears both in Requirements and Schedule; we should decide where to put it. Probably better to put travel under Schedule, as it's about schedule. Then Requirements would not include travel.
Let's allocate:
About the role: paragraph from "What You Will Do".
Responsibilities: bullet list from Key Responsibilities.
Requirements: mandatory qualifications: Public Trust clearance, Bachelor's degree, 5+ years experience in AI/data science/analytics/engineering, 3+ years experience in fraud/payment integrity role.
Qualifications: nice-to-have list.
Skills: technical skills list: development expertise in Python/R, SQL, data modeling; strong statistical and data analytics expertise; proficiency with various data structure formats; experience developing/testing/validating ML models at scale; experience with data visualization tools; experience applying data analytics/AI in financial crime compliance/fraud waste abuse prevention.
Pay: salary range.
Benefits: list of benefits.
Schedule: travel 25-50% for candidates not located in DMV/NYC.
We must ensure we don't create sections with filler like "commensurate with experience". We have real content.
Now we need to output HTML fragment only, no markdown fences.
We'll produce:
About the role
The AI/Data Senior Consultant leads discrete workstreams and delivers advanced AI and data solutions. This role combines technical depth, client engagement, and mentorship of junior team members while driving real‑world AI adoption.
Responsibilities
- Design and implement scalable AI and data solutions from requirements to deployment
- Develop, test, and tune fraud prevention and detection rules in a variety of programming languages
- Develop and optimize machine learning models and analytics workflows
- Lead data analytics efforts in detecting fraud, waste, abuse, and improper payments and identifying key areas to target for program/payment integrity improvements
- Partner with clients to identify AI use cases and translate business needs into solutions
- Own client deliverables and contribute to executive‑level presentations
- Mentor C2 consultants and review technical work
- Ensure solutions align with governance, security, and responsible AI standards (as applicable)
Requirements
- Must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding
About the role
with. Responsibilities: from Key Responsibilities. Requirements: maybe combine "What You Will Need" as Requirements? But we also have Qualifications, Skills sections. The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to decide what content goes where. We have: - About the role: description. - Responsibilities: list. - Requirements: maybe the mandatory qualifications (degree, years experience, public trust, etc.) - Qualifications: maybe the nice-to-have? Or could be separate. - Skills: maybe technical skills (Python/R, SQL, data modeling, etc.) but we already have some in requirements. Could separate. But we must not create sections with filler/placeholder text. Must have real specific content. We can structure as:
About the role
...
Responsibilities
- ...
Requirements
- ...
Qualifications
- ...
Skills
Maybe we could list technical skills like Python/R, SQL, data modeling, statistical analytics, data formats, ML model dev, visualization tools, etc. But those are already in requirements. Could combine. Alternatively, we could treat "What You Will Need" as Requirements and Qualifications combined, and then have Skills separate for technical proficiencies. But we must avoid duplication. Let's think: The posting includes: - Must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST"; ... (clearance) - Bachelor’s degree - FIVE (5) or more years of experience in AI, data science, analytics, or engineering - THREE (3) or more years of experience in a fraud/payment integrity-related role - Strong development expertise in Python/R, SQL, and data modeling - Strong statistical and data analytics expertise - Proficiency working with various data structure formats (CSV/TXT, JSON, XML, Parquet, etc.) - Experience developing, testing, and validating machine learning and other AI models at scale - Experience with data visualization tools (PowerBI, Tableau, Looker, etc.) - Experience with applying data analytics and artificial intelligence in the financial crime compliance and/or fraud, waste, and abuse prevention space - Travel will be 25-50% for candidates not located in the DMV or NYC area These are all mandatory requirements. Nice-to-have list: - Experience building fraud and/or fincrime monitoring rules in one or more programming languages - Proven ability to communicate complex concepts to non‑technical audiences - Experience working for or supporting healthcare technology, healthcare business or healthcare process initiatives - Cloud experience (AWS, Azure, GCP) including data and ML services - Experience deploying models into production (MLOps) - Experience with AI platforms (Databricks, Palantir, Vertex AI, SageMaker) - Prior consulting or client‑facing delivery experience - Federal or State Medicaid experience (directly as an employee or in a consulting role), particularly experience with fraud prevention and detection and/or program integrity experience with Medicaid We could put these under Qualifications. Skills: maybe we could list technical skills like Python/R, SQL, data modeling, statistical analytics, data formats, ML model dev, visualization tools. But those are already in requirements; we could still list them under Skills but that would duplicate content. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. Duplication is okay? It says preserve ALL substantive information; does not forbid duplication but better to avoid redundancy. However we need to follow the headings list; we could put skills under Skills heading, but then we would need to move those items from Requirements to Skills, leaving Requirements maybe just degree, experience, public trust, travel. But that would be splitting the original list; still preserving all info. Let's decide: We'll create: - About the role: description. - Responsibilities: list. - Requirements: mandatory items: Public Trust clearance, Bachelor's degree, 5+ years AI/data science, 3+ years fraud/payment integrity, Travel 25-50% (if not DMV/NYC). Possibly also include "Must be able to OBTAIN and MAINTAIN a Federal or DHS 'PUBLIC TRUST'..." etc. - Qualifications: nice-to-have list. - Skills: technical skills: Python/R, SQL, data modeling, statistical and data analytics expertise, proficiency with data formats (CSV/TXT, JSON, XML, Parquet, etc.), experience developing/testing/validating ML models at scale, experience with data visualization tools (PowerBI, Tableau, Looker, etc.), experience applying data analytics/AI in financial crime compliance/fraud waste abuse prevention. - Pay: salary range. - Benefits: list of benefits. - Schedule: travel %? Actually schedule could be travel requirement. But we already have travel in Requirements. However we could put travel under Schedule heading. The instruction says include a section ONLY when posting has real specific content for it. Travel is mentioned: "Travel will be 25-50% for candidates not located in the DMV or NYC area". That's specific. So we could put that under Schedule. Thus we need to avoid duplication: travel appears both in Requirements and Schedule; we should decide where to put it. Probably better to put travel under Schedule, as it's about schedule. Then Requirements would not include travel. Let's allocate: About the role: paragraph from "What You Will Do". Responsibilities: bullet list from Key Responsibilities. Requirements: mandatory qualifications: Public Trust clearance, Bachelor's degree, 5+ years experience in AI/data science/analytics/engineering, 3+ years experience in fraud/payment integrity role. Qualifications: nice-to-have list. Skills: technical skills list: development expertise in Python/R, SQL, data modeling; strong statistical and data analytics expertise; proficiency with various data structure formats; experience developing/testing/validating ML models at scale; experience with data visualization tools; experience applying data analytics/AI in financial crime compliance/fraud waste abuse prevention. Pay: salary range. Benefits: list of benefits. Schedule: travel 25-50% for candidates not located in DMV/NYC. We must ensure we don't create sections with filler like "commensurate with experience". We have real content. Now we need to output HTML fragment only, no markdown fences. We'll produce:About the role
The AI/Data Senior Consultant leads discrete workstreams and delivers advanced AI and data solutions. This role combines technical depth, client engagement, and mentorship of junior team members while driving real‑world AI adoption.
Responsibilities
- Design and implement scalable AI and data solutions from requirements to deployment
- Develop, test, and tune fraud prevention and detection rules in a variety of programming languages
- Develop and optimize machine learning models and analytics workflows
- Lead data analytics efforts in detecting fraud, waste, abuse, and improper payments and identifying key areas to target for program/payment integrity improvements
- Partner with clients to identify AI use cases and translate business needs into solutions
- Own client deliverables and contribute to executive‑level presentations
- Mentor C2 consultants and review technical work
- Ensure solutions align with governance, security, and responsible AI standards (as applicable)
Requirements
- Must be able to OBTAIN and MAINTAIN a Federal or DHS "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding
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