Senior Analyst, Data & Analytics (AI)

Senior Analyst, Data & Analytics (AI) About Palm Tree Palm Tree is a modern M&A value creation firm that integrates financial consulting, operational consulting, and investment banking services. Founded in 2010, Palm Tree partners with private equity firms, business operators, and management teams through strategic events including acquisitions, carve‑outs, recapitalizations, restructurings, integrations, and performance improvement initiatives. 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 Data Engineering & Pipeline Development 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 Business Intelligence & Reporting 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 Client Engagement & Analytics Strategy 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 Team Development & Practice Growth 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 Qualifications 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 Preferred Experience 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 Compensation and Benefits 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 #J-18808-Ljbffr