Vetted Power BI Professionals

Zahara Kanji

Screened

Director-level HR People Partner specializing in tech org design, workforce analytics, and talent strategy

21y exp

“People/HR leader partnering closely with CTO and senior engineering leadership on major org transformations, including decentralizing QA/IT Ops and establishing new performance/promotion guardrails. Built and scaled a Returnship program across a tech org (60+ conversions to FTE; +40% diversity hiring) and used people analytics to cut high-performer turnover from 18% to 5% while boosting internal promotions to 20%. Experienced in executive coaching, succession planning, and sensitive ER investigations in partnership with legal/ER.”

“Engineering leader who built and delivered an IoT smart-spaces platform for the self-storage and smart-living domains, translating customer requirements into architecture, capability maps, and a multi-milestone roadmap. Personally stood up missing AI/ML capabilities (including churn prediction) using Databricks (Delta Lake/MLflow), enabling follow-on features like energy optimization and security/anomaly detection. Scaled an org from 20 to 80+ with disciplined Agile planning (Jira Advanced Roadmaps/Confluence) and strong executive/customer-facing leadership during high-stakes customer commitments.”

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

“Talent Acquisition leader in professional services/consulting who has led national experienced hire and campus recruiting teams at scale (up to ~40 recruiters). Known for data-driven recruiting strategy—cut a campus school roster by ~60% based on ROI analysis, expanded virtual sourcing, and reduced spend—plus executive-facing workforce planning, talent model redesign, and interview process modernization (virtual, pre-recorded, psychometrics).”

“PMO consultant with experience delivering regulatory and compliance-driven programs across financial services and government healthcare, including Citibank MRA remediation (Helios) and Ohio Department of Medicaid enterprise release management. Known for proactively consolidating fragmented project data into executive-ready reporting, driving cross-functional alignment, and operating with strong discretion in regulated environments.”

Tina Bai

Screened

Executive operator-founder specializing in GTM, revenue strategy, and AI-native commercial operations

New York, NY10y exp

“Operator with Deloitte consulting experience supporting PE/VC-backed portfolio companies and current experience inside a VC-funded Series C scale-up. Building a bootstrapped D2C women’s telehealth brand (Petty Pills) with two initial SKUs, deployed tech stack, and white-labeled clinical/compliance infrastructure, leveraging AI tooling to keep operations lean while focusing on full-time operator roles.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

Yishi Wang

Screened

Junior Machine Learning & Data Science professional specializing in LLMs and analytics

Chicago, IL3y exp

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”