Andrii F.

Ready to dive into your data deeply Make data analysis and predictive modelling Marketing and business domains are preferred Proficient in customer and investments analytics ✅ EXPERT-VETTED TOP 1% ON UPWORK 🚻 Customers and growth analytics Calculation different customer KPI like LTV Segmenting customers by using the RFM method Customers cohorts analysis to detect churn Scoring customer and revenue churn risks Predicting dates and prices of future orders Modeling prices and revenue flow scenarios Analyzing user behavior on social media 🛒 Products and inventory analytics Forecasting stock-out events to minimize lost of sales Planning assortment based on ABC & XYZ analysis Assessing product competitiveness on the market Market basket analysis based on association rules Product sentiment analysis based on social media data 🏦 Venture capital investment analytics Deep understanding of crunchbase data source Scoring startups' success, multiples, and time of exit Startup market analysis and trends insights Modelling investment deals processes Design due diligence recommendations 📶 Trading analytics Designing and verifying trading strategies Modelling reflexive behavior strategies Recognizing threat patterns in trading data Detecting anomalies and outliers in trading data Managing trading deals and assessing associated risks Assessing fraud risks in trading and investment activities Algorithmic trading strategies design for crypto market 🚀 Google Analytics Defining click-through-rate drivers Assessment listings search sort relevance Scoring and ranking website users' engagement Calculations of normalized discounted cumulative gain 🧪 Clinical Trials Analytics Design R Shiny dashboards for analytical results reporting Redesign legacy R and R Shiny code to have more robust apps TECH STACK 🔧 Core Languages Python (primary) – ML systems, data engineering, APIs, AI and LLM R (advanced) – R Shiny apps, analytics, reporting, statistical modeling SQL (expert) – 20+ years, complex analytics & data modeling 🎛 Data Science & Machine Learning Data analysis: tidyverse, data.table, lubridate Modeling: tidymodels, caret, mlr3, parsnip, broom Tree-based models: ranger, randomForest, xgboost, catboost Time series: forecast, prophet, tidyverts, xts, zoo NLP: tidytext, quanteda, sentimentr, stm Quant finance: quantmod, quantstrat Pharma analytics: pharmaverse, rtables, admital, tidytlg 👨‍💻 Data Apps, APIs & Reporting Dashboards: shiny, shinydashboard, bslib, shinyjs App frameworks: golem, rhino, teal Tables: DT, reactable, gt, flextable Reporting: RMarkdown, bookdown, LaTeX Auth: shinymanager, shinyauthr Mobile apps: shinyMobile APIs: plumber 💾 Data Engineering & Storage Databases: RMySQL, RODBC, bigrquery Cloud & formats: aws.s3, jsonlite, feather, openxlsx Distributed: sparklyr, SparkR, h2o Platforms: Databricks, Snowflake, Palantir Foundry, AWS 📶 AI, LLMs & MLOps LLM apps: ellmer, querychat, shinychat,mini007 Shared: Experiment tracking: MLflow 🤹 Visualization Core: ggplot2, ggpubr, ggmap Networks: igraph, ggnetwork, geomnet Hierarchies: treemap, data.tree JS integrations (via R) plotly.js, d3.js, highcharts, leaflet, networkD3 ⚙️ Software Engineering Packaging: devtools, renv, packrat Performance: Rcpp, RcppParallel, microbenchmark Parallel computing: foreach, doParallel ⏱️ Trading & Quant Systems Research & backtesting: quantmod, quantstrat