𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝘁𝗼𝗱𝗮𝘆 May 5 💡 𝐅𝐫𝐞𝐞 𝟑𝟎-𝐦𝐢𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐜𝐚𝐥𝐥: I’ll show you how to unify Power BI, SQL, ETL, and warehouse reporting into one trusted source of truth. Get answers in minutes, remove manual reporting, and give leadership real-time visibility. I’m a Business Intelligence Specialist with over 12 years of experience designing, building, and optimizing enterprise-grade data systems. I specialize in cloud architecture (AWS, GCP, Azure), data warehousing, and BI solutions that help businesses unlock insights, cut costs, and scale with confidence. I’ve delivered projects across 𝐟𝐢𝐧𝐚𝐧𝐜𝐞, 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞, 𝐞-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞, 𝐚𝐧𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡, with a strong focus on security, performance, and business impact. 𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐚𝐭 𝐈 𝐂𝐚𝐧 𝐃𝐨 𝐅𝐨𝐫 𝐘𝐨𝐮: - Design and implement data warehouses and lakes using best practices (Kimball, BIML, SQL Server, Snowflake). - Develop ETL/ELT pipelines with SSIS, Python, and cloud-native tools for reliable, automated data flows. - Build interactive dashboards and analytics in Power BI, helping teams move from raw data to actionable insights. - Run security reviews and cost optimization across cloud platforms to reduce spend without compromising performance. - Deliver custom CRM and analytics solutions tailored for industries like pharma, pensions, and financial services. 𝐒𝐤𝐢𝐥𝐥𝐬 𝐓𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫: - Cloud & DevOps: AWS, GCP, Azure, Terraform, CI/CD, GitHub - Data Engineering & Warehousing: SQL Server (SSIS, SSAS, SSRS), Snowflake, Data Lakes, Kimball Methodology - Analytics & BI: Power BI, DAX, SQL, Python - Security & Optimization: Cloud security reviews, cost reduction strategies 𝐓𝐨𝐨𝐥𝐬 𝐈 𝐔𝐬𝐞 Power BI, SQL Server, Python, Snowflake, Terraform, AWS, GCP, Azure, React, React Native, GitHub CI/CD, SSIS, SSAS, SSRS 𝐖𝐡𝐚𝐭 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐒𝐚𝐲 “Delivered a payment processing solution handling £10M+ monthly transactions with zero downtime. Reliable, detail-oriented, and excellent communicator - will rehire for our next project.” If your dashboards are unclear, your data pipelines fragile, or your cloud costs out of control, I’ll fix that. 📩 𝐒𝐞𝐧𝐝 𝐦𝐞 𝐚 𝐦𝐞𝐬𝐬𝐚𝐠𝐞, 𝐚𝐧𝐝 𝐰𝐞’𝐥𝐥 𝐛𝐨𝐨𝐤 𝐲𝐨𝐮𝐫 𝐟𝐫𝐞𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐜𝐚𝐥𝐥.

I am an Applied AI & Machine Learning Consultant with a strong background in data analytics, predictive modeling, statistical analysis, and decision support systems. My experience spans finance, lending, capital markets, marine analytics, retail, and business intelligence, where I have developed data-driven solutions that improve forecasting accuracy, automate analytical workflows, and support strategic decision-making. My core expertise lies in transforming raw data into actionable insights through data preparation, feature engineering, statistical modeling, machine learning, and interactive analytics. I enjoy working on challenging analytical problems, particularly those involving predictive modeling, time-series forecasting, risk assessment, and large-scale structured datasets. My focus is always on building practical, production-ready solutions rather than purely theoretical models. I have hands-on experience developing regression, classification, ensemble learning, and forecasting models using Python and R. My work includes model selection, hyperparameter optimization, feature engineering, model validation, explainability, performance monitoring, and deployment-ready analytics pipelines. I am comfortable working with algorithms such as XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting, Logistic Regression, Elastic Net, Support Vector Machines, Neural Networks, and time-series techniques including ARIMA and GARCH, while selecting the most appropriate approach based on business objectives and data characteristics. My experience also includes financial and quantitative analytics, where I have built predictive models and analytical dashboards for mortgage-backed securities, probability of default, options analytics, stock market analysis, foreign exchange research, and market intelligence. These projects strengthened my understanding of financial datasets, feature engineering for market data, risk modeling, and the importance of robust validation techniques for real-world decision making. Beyond predictive modeling, I develop interactive analytical applications and dashboards using Streamlit and R Shiny, enabling stakeholders to visualize complex datasets, monitor model outputs, and interact with analytics through intuitive web-based interfaces. My technical toolkit includes Python (Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch, SciPy, Statsmodels, Plotly, Streamlit, FastAPI), R (tidyverse, caret, glmnet, forecast, Shiny), SQL, Power BI, Tableau, Git, Jupyter Notebook, Google Colab, and cloud-based analytics environments. I also recently expanded my expertise into modern AI applications, prompt engineering, AI workflow automation, agentic AI, AI productivity systems, and practical enterprise adoption of Generative AI technologies. This complements my existing machine learning experience by helping organizations combine traditional predictive analytics with modern AI-powered workflows. I believe AI should be applied thoughtfully. I use classical statistical and machine learning methods when they provide the best predictive performance, and leverage Generative AI where it genuinely improves productivity, automation, knowledge extraction, or decision support. My approach emphasizes explainability, reproducibility, robust validation, and solutions that deliver measurable business value. Whether the objective is predictive analytics, statistical modeling, machine learning, AI-assisted automation, financial analytics, business intelligence, or interactive dashboards, I enjoy collaborating with clients to build scalable, reliable, and data-driven solutions that solve real business problems.

Do you need help analysing public health or research data and presenting the results clearly? I work in public health research, biostatistics, and epidemiology, and I have over 3 years of experience working with research and health related data. I support researchers, NGOs, and students by helping them analyse data correctly and explain the results in a clear and simple way. I work with tools such as R, Stata, SPSS, ATLAS.ti, SAS, EViews, Excel, Tableau, and REDCap, depending on the needs of the project and the type of data involved. Here is what I can help you with: ✅ Data cleaning and data preparation ✅ Biostatistics and epidemiological analysis ✅ Public health and medical research analysis ✅ Survey data analysis ✅ Clinical trials data analysis ✅ Survival analysis, including Kaplan Meier curves and Cox regression ✅ Systematic reviews and meta analysis ✅ Tables, figures, and clear result summaries I communicate clearly, ask the right questions, and keep the process simple and organised. If you already have data, I can start right away. If you are still unsure about the analysis, I can help you think it through step by step. I will be happy to discuss your project and see how I can help.

With a Ph.D. in Statistics and over 14 years of academic and research experience, I provide expert-level statistical consulting and data analysis services for both academic and industry-based projects. Currently serving as an Assistant Professor in Statistics, I specialize in transforming raw, complex data into clear, actionable insights that drive results. Tools & Technologies I Use: SPSS, R Studio, Python (Pandas, Scikit-learn, Statsmodels), SAS Studio, MS Excel (Advanced Analytics), Jamovi, Stata, Systat Services I Offer: Hypothesis Testing & Inferential Statistics Regression, ANOVA, Correlation, Chi-square Exploratory & Confirmatory Factor Analysis (EFA/CFA) Machine Learning & Predictive Modeling Survey Design & Likert Scale Analysis Thesis/Dissertation Support (Statistical Chapters & Interpretation) Data Cleaning, Visualization & Report Writing Industries I Serve: Healthcare & Clinical Research (biostatistics, RCT analysis, patient data) Education & Social Sciences (survey studies, experimental designs) Business & Marketing Analytics (consumer behavior, A/B testing, forecasting) Finance & Economics (risk analysis, time series forecasting, econometrics) Psychology & Behavioral Science (scale validation, SEM, psychometrics) Public Policy & Government Research (sampling methods, demographic analysis) Manufacturing & Quality Control (DOE, control charts, Six Sigma tools) Client-Centric Approach: I’m committed to delivering accurate, timely, and impactful solutions — whether it’s a one-time analysis or long-term support. I’ve worked with Ph.D. scholars, researchers, startups, and professionals across the globe. Rate Flexibility: I value long-term professional relationships and client satisfaction, and I am willing to work at your proposed rate to ensure we achieve the best outcomes together. Now bringing my services to Upwork, I aim to deliver accurate, reliable, and impactful statistical solutions always on time and tailored to your unique goals. Let’s connect and discuss how I can help bring your data to life.

I’m a biostatistician and statistical programmer with four years of experience transforming messy, complex datasets into clear, actionable insights. Whether you're a startup needing dashboards, a small business looking to make sense of your customer data, or a researcher needing statistical analysis, I'm here to help. My toolbox includes: - Languages & Tools: R, SAS, Python, SQL, and Excel - Skills: Data wrangling, visualization, statistical modeling, automation, reporting - Experience: Survey data, time-series, A/B testing, dashboards, public health data, and more I’m especially good at: - Cleaning and structuring raw data to make it analysis-ready - Building intuitive dashboards and data visualizations - Running regression models, classification algorithms, and predictive analytics - Writing clear, understandable reports—even for non-technical audiences - Delivering on tight deadlines with a collaborative, responsive approach 📌 Bonus: I’m bilingual (English & Spanish), detail-oriented, and love solving problems that others find overwhelming. Let’s turn your data into decisions. Reach out and let’s chat about your project!

Richard Xue: A Senior Leader in Global Clinical Development Richard Xue is a highly accomplished clinical development executive with over 18 years of extensive experience spanning global pharmaceutical corporations and local biotech companies. He has a distinguished reputation for his strategic leadership, profound therapeutic expertise, and a proven track record of successfully guiding novel therapeutics and medical devices from early-stage development through to regulatory approval. Mr. Xue's career is characterized by his ability to build, lead, and manage high-performing, multidisciplinary clinical development organizations. He has held senior leadership positions with overarching responsibility for clinical operations, biometrics, medical affairs, and regulatory functions. His leadership has been instrumental in establishing and leading Regional Clinical Development organizations in the Asia-Pacific region, driving strategy and execution across diverse markets. He possesses deep, hands-on experience across a broad spectrum of therapeutic areas, including Oncology (e.g., NSCLC, cHL, Melanoma, various solid tumors), Ophthalmology, Infectious Diseases, Neuro-degenerative Diseases (e.g., ALS, Alzheimer's), Autoimmune/Immune Diseases, and Diabetes. This expertise encompasses the clinical development of biologics, small molecules, biosimilars, and medical devices. Mr. Xue's late-stage development and New Drug Application (NDA) approval experience is substantial. He has played a key role in the approval of numerous products, including the biologics Zimberelimab, the small molecules Tunlametinib and Lefamulin, various ophthalmic solutions (Dutrav, Vigamox, etc.), the biosimilar Basaglar, and medical devices like the Centurion Silver vision system and Panoptix intra-ocular lens. His early-stage development prowess is equally impressive, having successfully initiated and completed multiple First-in-Human (FIH) studies for innovative small-molecule compounds and possessing extensive Phase II and Investigator-Initiated Trial (IIT) experience. He is adept at crafting clinical strategies and developing protocols for a wide array of complex indications. Throughout his career, Mr. Xue has consistently demonstrated his ability to deliver results in demanding roles: As VP and China Head of Clinical Operation, Biometrics and Regulatory at Sironax, he successfully initiated and completed 5 FIH studies, conducted global Phase IIa studies, obtained 4 IND approvals, and built a comprehensive clinical quality system and a dynamic team from the ground up. At Kechow Pharma as Global Head of Biostatistics, Data Management & Medical Writing, he established a lean biometrics team and made significant contributions to the pivotal study and NDA approval of Tunlametinib. During his tenure as Head of Clinical Project Strategy, Biostatistics, & Data Management at Gloriabio, he strongly supported the NDA application and approval of Zimberelimab and led multiple clinical development strategies. As VP & Head of Clinical Operations/Biometrics/Portfolio Management at Sinovant Sciences, he founded multiple departments, secured 3 IND approvals within a year, and laid the foundation for the NDA of Lefamulin. In his nearly seven years at Alcon, progressing to Head of Clinical Development, Asia, he led or significantly contributed to the approval of over 10 drug or device products and successfully turned around NDA approvals in India. His foundational roles at Eli Lilly and PAREXEL provided him with deep, hands-on expertise in biostatistics, data management, and regulatory interactions, forming the bedrock of his later leadership success. Richard Xue holds a Doctorate in Health Care Management from Universidad Católica San Antonio, Spain, and dual Master of Science degrees in Biostatistics and Applied Statistics from the University of Hasselt, Belgium. He also completed post-graduate work in Applied Computer Science at Vrije Universiteit Brussels, Belgium. This robust educational background underpins his data-driven and strategic approach to clinical development, making him a respected leader capable of navigating the complexities of the global pharmaceutical and biotech landscape.

Accomplished Marketing Analytics professional with extensive experience in Consumer Insights, Market Research and Predictive Modeling across Retail, Foodservice and DotCom industries. Strong track record of translating insights into actionable business strategies that deliver a competitive advantage. A proven team-builder who is able to articulate a vision to achieve alignment and consensus. Exceptional communication skills that drive engagement toward achieving results. Proficient with SAS; SPSS-Statistics; SPSS-Modeler; Tableau; SQL/MYSQL; Microsoft Office and Qualtrics’ survey software.

Do you need accurate, actionable insights from your survey, research, or business data to make informed decisions through powerful statistical and data science analysis? Look no further! I am a top-rated and full-time research data analyst with >8 years of experience in IBM SPSS, R, Python, SAS, STATA, NVIVO, JMP, JASP, Atlas.ti, Excel/Google Sheets. As a statistics wiz, survey expert, and data science practitioner, my areas of expertise include: ⭐️ Questionnaire/survey design & validation ⭐️ Survey & research data analysis: descriptive, inferential (Chi-Square, regression, correlation, t-test, ANOVA, etc.), predictive modeling ⭐️ Detailed interpretation & storytelling of results ⭐️ Report writing, academic formatting (APA, Harvard), and publication-ready outputs ⭐️ Advanced automation & scripting for reproducible workflows I provide a variety of services including but not limited to: ★ Sample size calculations ★ Correlation, regression, & multivariate analysis ★ Hypothesis testing & statistical modeling ★ Factor analysis (EFA/CFA), structural equation modeling ★ Predictive analytics & data visualization ▶︎ and much more My specializations: Health Research & Predictive Analysis Master's/Ph.D. Thesis & Dissertation Modeling Company Survey & Performance Analytics Social, Economic & Behavioral Data Science Projects What clients say 👉👉👉 ☆☆☆☆☆ "She is always of great help, and the turnaround time is on point. I can depend on her for assistance and explaining the work thoroughly." "Great work. Amazing communication and delivered the project on time." "Esther's results were clear, organized, and thoroughly explained. Excellent work! I'm so impressed! I appreciate her efforts and insight to the analysis of the project!" ☆☆☆☆ ✍️ Feel free to get in touch, and we can discuss your project further

With 15+ years as a Data Engineer and M.S. in Math, my experience includes the development of ETL/Data Warehouse and Analytics dashboards for eCommerce, Marketing, and Banking. 𝙎𝙪𝙘𝙘𝙚𝙨𝙨 𝙎𝙩𝙤𝙧𝙞𝙚𝙨 1. Payments forecast with MAPE < 2%: Banks need to know their monthly cash inflow to plan new credits and attract investments. I have compiled historical payments from existing clients, organized by calendar month, loan date, and credit product. Used this info to iteratively forecast new payments from new credits. 2. Data Quality from 24% to 99%: fixed core table with account balances in a banking DWH. Analytics accounts in accounting equal the sum of the basic accounts. I made a list of clients with analytics and basic accounts that mismatch. Picked one basic account with the most zero balances across all clients. Statistically, it had the most chances of being wrong. Discussed it with the Admin of the source system, and after several find-and-fix iterations, he confessed there is a filter that does not export all the data into the DWH, enforced by security guys. 3. Credit scoring models development, reducing scoring costs by half: A Fintech company was looking for software to build an internal credit score. They were considering SAS, SPSS, and other software. I proposed to them to build a scoring model using the free R-project, and I was hired immediately. Have joined applications and credit histories data and built scoring models using logistic regression. After the AB test with underwriters, the model proved its efficiency and was deployed in production. We stopped paying for credit scores from 3d party provider reducing scoring costs by 2 times. 4. I was optimizing the legal collection process for a bank. The overall collection process starts with contacting the debtor and discussing the payment plan. If the debtor is unavailable or refuses to pay, the legal collection process starts. Lawyers fill in applications for the Magistrates’ court, paying some court fees. The court may decline the application if it reaches the court too late due to the statute of limitations or take the debtor’s side. Even a won case does not guarantee the debtor will pay. He might decide to file for bankruptcy. Given the high number of debts and fees involved in the court applications, legal collection is a costly process. Multiple outcomes make this process manually unpredictable. I was tasked with building a collection scoring model to filter those debts that are worth taking legal actions. I have pulled information on 100K debts that went through the legal collection process past 3 years. My target variable was the sum of payments after legal actions minus legal fees. I have tried many predictor variables, and the most informative were: • debt amount - the higher the debt, the higher the payment amount is expected • days past due - the higher the DPD, the higher the chances of hitting the statute of limitations • debotr’s region - courts' practices vary across the country, with some courts taking debtors’ side more often Once the model has been built, we applied it to the current debts in the legal process and found out that only 30% of debts are worth taking to court. The rest 70% could be sold to collection agencies. The legal collection scoring model helped to understand: • the factors that drive collection profits • automate manual decisions • increase collection profits • clear debts balances, selling them to professional collection agencies 𝙏𝙤𝙤𝙡𝙗𝙤𝙭 • ETL/Data Warehouse: SQL, dbt, Python, Redshift, BigQuery, SAS • Data sources: Shopify, GA4, Google Ads, Meta Ads • Analytics dashboards: Looker Studio, Looker, Power BI, Excel, Google Sheets • Finance math: ROAS, Gross margin, LTV, Cash Recovery • Data Science and ML: Credit Scoring and payments forecasting • Cloud tech: AWS, GCP, Docker • Technical writing: Business Proposal, Product Description, User's Guide, Installation Guide • Project management: Agile / Scrum Let's connect to discuss the details.

Customer lifetime value (CLV/LTV) modeling is my specialty. Using probability models (BG/NBD, Pareto/NBD, Gamma-Gamma), retention curves, and RFM segmentation, I tell you who your most valuable customers are, what they are worth, and how to acquire more of them. Around that core I bring the full marketing-analytics toolkit - marketing mix modeling (MMM), multi-touch attribution, pricing, and forecasting - in Python and R, for CPG, DTC, media, fintech, and B2B clients. Over 30 years I have directed more than $70M in combined consumer-insights and analytics spend - $20M+ client-side (Bacardi Global Brands) and $50M+ agency-side (GfK, Millward Brown/Kantar) - so I read these models the way a CMO does, not just a data scientist. I translate model output into the decision, the deck, and the board-ready story. What I deliver: - Customer lifetime value (CLV/LTV): BG/NBD and Pareto/NBD + Gamma-Gamma probability models, retention curves, cohort and RFM segmentation; forecast customer value and surface your most valuable acquisition cohorts for DTC and subscription brands. - Marketing mix modeling & media ROI: ARIMAX, Bayesian/hierarchical, and regression models with adstock and saturation; in a recent engagement I identified a channel-reallocation opportunity worth roughly a 185% gain in marketing efficiency. Build from scratch, or finalize, validate, deploy, and maintain existing models (Python or R). - Attribution & measurement: reconciling Meta / Google / GA4 platform reporting against actual business outcomes; blended CAC and single-source-of-truth frameworks. - Pricing & forecasting: conjoint / willingness-to-pay studies and supervised-ML pricing models (random forest, gradient boosting) deployed as interactive calculators. - Data engineering & viz: BigQuery warehousing, dashboards (Power BI, Tableau, Looker Studio), and clear recommendations leadership can act on. Credentials: Wharton MBA (Marketing), Brown B.A. in Applied Mathematics. Top Rated on Upwork with 100% Job Success across 8 engagements. Stack: Python, R, SQL, SAS, Azure, AWS, GCP/BigQuery, Power BI, Tableau, GA4, Looker Studio. Fluent in Spanish, working Portuguese - comfortable on global, multi-market engagements. I keep scope tight, communicate in business terms, and hit deadlines.