Senior Analyst, Data & Analytics (AI)
Indexed description
Palm Tree’s Data & Analytics practice helps private equity portfolio companies and middle-market businesses build modern, insight-driven data capabilities. Our team works alongside executive leadership to design, implement, and optimize data infrastructure and analytics solutions that provide real-time visibility into operational and financial performance.
The Senior Analyst Position
The Senior Analyst is a practitioner-level contributor within Palm Tree's Data & Analytics practice. Senior Analysts build and deploy analytics and machine learning solutions across active client engagements, driving measurable operational and financial outcomes for PE-backed portfolio companies.
This role blends hands-on technical development with applied AI/ML work. Senior Analysts design and implement predictive models, automate analytical workflows, and develop scalable data infrastructure that powers real-time reporting and advanced analytics. The role is embedded in client delivery and requires direct engagement with business stakeholders.
The ideal candidate has a strong foundation in data engineering and BI, a working command of machine learning methodologies, and the ability to translate analytical outputs into clear business recommendations. Experience in consulting, PE, or fast-paced operational environments is a strong plus.
Core Responsibilities
AI/ML Model Development & Deployment
- Build and deploy supervised and unsupervised ML models including forecasting (ARIMA, VAR, gradient boosting), classification, clustering, and anomaly detection across client datasets
- Design and implement NLP pipelines for topic modeling, text classification, and unstructured data analysis on operational and financial records
- Develop and maintain experimentation frameworks including A/B testing, lift analysis, and causal inference to evaluate operational interventions
- Translate model outputs into actionable business recommendations for operations, finance, and commercial leadership teams
- Apply imbalance correction, cross-validation, and threshold optimization to ensure model reliability in production environments
- Design and build scalable ETL/ELT pipelines using Azure Data Factory, Python, and cloud-native tools to ingest data from ERP, CRM, and operational source systems
- Write production-grade SQL to model, transform, and validate data across complex multi-table schemas
- Architect cloud data infrastructure on Azure (Azure SQL, ADLS, ADF) and Snowflake to support analytics and ML workloads
- Reduce data latency and improve pipeline reliability through automated orchestration and monitoring
- Develop and maintain Power BI semantic models, DAX measures, and executive dashboards that surface operational and financial KPIs
- Design KPI reporting frameworks providing leadership teams with real-time visibility into performance across inventory, revenue, and operations
- Implement row-level security, governance controls, and data quality checks within BI environments
- Partner directly with portfolio company stakeholders to define analytical requirements, success metrics, and delivery frameworks
- Translate complex business questions into data models, ML problem statements, and analytical frameworks
- Lead working sessions with business and technical stakeholders to align on priorities and communicate findings clearly
- Independently manage analytics workstreams including scoping, effort estimation, and delivery execution
- Mentor and support junior analysts across client engagements
- Contribute to internal knowledge development including ML frameworks, data architecture standards, and reusable analytics assets
- Support development of AI/ML methodologies and delivery accelerators within the practice
- 3-5+ years of experience in data analytics, data science, or data engineering roles
- Proficiency in Python for ML model development (scikit-learn, statsmodels, NumPy, Pandas) and data pipeline automation
- Strong SQL proficiency including CTEs, window functions, subqueries, and complex joins across large-scale schemas
- Hands-on experience building and deploying machine learning models in production or near-production environments
- Experience with forecasting methodologies (ARIMA, VAR, gradient boosting) and/or NLP techniques (LDA, text classification)
- Demonstrated ability to design and evaluate controlled experiments including A/B testing and lift analysis
- Experience building Power BI models, DAX measures, and dashboards for operational or financial reporting
- Strong communication skills with the ability to translate technical findings to non-technical business audiences
- Ability to independently manage multiple workstreams in fast-paced consulting or advisory environments
- Experience working within private equity portfolio company environments or management consulting
- Hands-on experience with Azure Data Factory, Azure SQL, ADLS, or Snowflake for cloud data infrastructure
- Exposure to ERP systems such as AS/400, NetSuite, QuickBooks, or similar platforms
- Familiarity with BI tools such as Tableau, Power BI, or Databricks for analytical delivery
- Experience with R for statistical modeling or time-series analysis
- Graduate degree in Analytics, Statistics, Computer Science, Economics, or a related quantitative field
- Base salary range of $100,000-$125,000, with performance-based bonus opportunities
- Comprehensive benefits package including medical, dental, and vision insurance
- Competitive 401(k) program with employer matching contributions
- Hybrid work environment with access to offices in Los Angeles, Chicago, and New York
- Unlimited paid time off (PTO)
- Opportunities for career advancement within a merit-based, entrepreneurial culture
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