available for remote, entry-level people analytics / hr ai roles
andrew allen
i build privacy-minded, audit-friendly ai and analytics tools for hr and workforce decision support—pairing
measurement (people analytics) with responsible ml (fairness, explainability, governance).
i bring a business management foundation plus hands-on engineering experience shipping real software.
focus
people analytics + hr science, implemented with responsible ai
what i build
- workforce & people analytics: retention/attrition modeling, engagement measurement, and workforce planning support.
- hr nlp: analysis of surveys, feedback, job descriptions, and other hr text—designed to be privacy-aware.
- skills intelligence: skills inference from resumes/jds and skills gap framing for workforce planning.
- fairness & auditability: bias testing, clear evaluation, and documentation for employment-context ai.
- decision support tooling: analytics + explanations + governance guardrails for hr partners and leaders.
target roles
remote, entry-level roles where i can combine engineering execution with hr measurement thinking.
- people analytics data scientist (engineering-leaning)
- hr / workforce analytics analyst (ai-forward)
- hr data scientist (applied ml for hr)
- talent analytics specialist
- applied scientist (people analytics)
highlights
high-signal strengths for people analytics engineering
product-minded builder
shipped ios apps as a solo developer—real constraints, real users, real performance and reliability tradeoffs.
measurement + rigor
prioritizing statistics and probability to support trustworthy hr measurement and model evaluation.
responsible ai mindset
sensitive-data awareness from security coursework; focus on privacy, governance, and avoiding overclaims.
- pragmatic communicator: clearly separates what’s proven vs. in-progress.
- systems thinking: comfortable reasoning across software, data, and deployment constraints.
- automation mindset: uses powershell to make workflows repeatable and auditable.
selected projects
proof over chronology
people_ai — people analytics for growth (flagship direction)
what it is: a product direction focused on pairing clear measurement with coaching-style support so people can build professional skills,
emotional skills, and values—while keeping sharing optional.
- what i’m doing
- designing an hr-safe analytics concept: evidence people can own, privacy-minded defaults, and governance-aware ux.
- technical themes
- rag patterns for policy/knowledge support, evaluation discipline, and responsible ai documentation (learning + build planning).
- outcome / artifact
- in progress: architecture notes and an implementation plan (no claims until shipped and documented).
people analytics
responsible ai
rag (planned)
governance
privacy-minded
- what i did
- solo development; designed the interaction model, implemented core algorithms, and optimized for responsive drawing.
- stack
- swift, uikit (with prior objective-c experience in the pre-swift era).
- why it matters
- demonstrates product engineering: performance, reliability, and user-centered iteration—skills that transfer directly to analytics tooling for hr stakeholders.
ios
swift
uikit
algorithms
performance
- what i did
- implemented timing logic and mitigations for background interference; used concurrency strategies where reliability mattered.
- stack
- ios app development (implementation details available on request).
- why it matters
- shows correctness under constraints—directly relevant to trustworthy analytics pipelines and model evaluation.
reliability
concurrency
performance
engineering rigor
- what i did
- implemented animation logic and gameplay physics with attention to clarity and correctness.
- stack
- java
- why it matters
- reinforces cs fundamentals that support ml/analytics work: state, logic, and disciplined implementation.
java
algorithms
cs fundamentals
- what i did
- built scripts for system inventory/diagnostics, storage cleanup, and repeatable environment tweaks; configured local vm networks for labs.
- stack
- powershell, windows/vm networking labs
- outcome / artifact
- working scripts and repeatable lab setups (not published).
powershell
automation
networking
virtualization
- what i did
- working through modules: planning and building a rag-based solution with user data, with a focus on evaluation and safe deployment patterns.
- stack
- microsoft learn (azure ai / foundry learning path; module work in progress).
- outcome / artifact
- in progress; not claiming results until completed and documented.
rag
llms
azure ai (learning)
responsible ai
experience
curated to support a people analytics / hr ai narrative
independent software development
shipped ios applications as a solo developer, emphasizing performance, correctness, and user interaction.
- designed and implemented core algorithms and data models for interactive features.
- worked through performance constraints and complexity tradeoffs to keep apps responsive.
- used concurrency and timing strategies where reliability mattered.
people analytics + ai upskilling
building the foundations to apply ml responsibly in hr contexts—prioritizing measurement, evaluation, and clear communication.
- coursera: generative ai & llm architecture/data prep; quantitative modeling foundations.
- microsoft learn: ai fluency trophy; responsible ai modules; azure ai planning/build modules (in progress).
- math practice: statistics & probability (priority), plus linear algebra and calculus refresh.
it labs & scripting (coursework + practice)
practical experience with windows server administration topics, networking, virtualization, linux familiarity, and automation via powershell.
skills
grouped for clarity
people analytics / hr science
- measurement mindset: kpis, leading indicators, data quality
- predictive modeling themes: retention/attrition, funnels, segmentation
- nlp themes: surveys, feedback, job descriptions (privacy-aware framing)
- responsible ai: fairness, bias auditing, explainability, governance
- stakeholder communication: clear, non-hype explanations
ai / ml foundations
- machine learning & ai basics (actively strengthening)
- deep learning basics (foundational)
- llm/rag patterns (learning + build planning)
- math focus: statistics & probability (in progress)
engineering & systems
- swift, uikit (shipped apps)
- java (course/project work)
- algorithms, data structures, complexity tradeoffs
- powershell scripting/automation
- virtualization + networking labs; linux familiarity
- security fundamentals (coursework)
education & credentials
included where it strengthens positioning
education
- mitchell technical college — aas, business management (graduated may 2011)
- dakota state university — ai-related coursework (spring & summer 2025; topics: ai, statistics, linear algebra, discrete math, cs)
- mitchell technical college — additional study (partial): it (windows server administration, scripting, networking, virtualization, linux); scada (1 semester)
selected coursework / credentials
- coursera (ibm) — generative ai and llms: architecture and data preparation (completed jan 2026)
- microsoft learn — ai fluency trophy (account: andrewallen-8471)
- coursera (university of pennsylvania) — fundamentals of quantitative modeling (completed aug 2024)
- coursera (cisco) — soc (sep 2025) + security series (oct 2025)
note: only completed or clearly documented items are marked as completed.