Hire the Best Time Series Analysis Freelancers in California

I help businesses, researchers, and finance teams turn complex or messy data into reliable datasets, predictive models, dashboards, and actionable insights. I am a machine learning engineer and an Economist who works across the full analytical pipeline, from raw messy data to deployed production system. My two most recent projects are not portfolio screenshots, they are live systems anyone can open in a browser right now. MacroSense is a production Machine Learning forecasting system that predicts US GDP growth, inflation, and unemployment six months ahead using Federal Reserve data. Built across a rigorous six-stage pipeline covering API data collection, exploratory analysis, feature engineering with stationarity transformation and lag variables, ensemble modelling, and walk-forward validation across 25 years of economic history. Directional accuracy of 88.9% for GDP forecasting. Live on Streamlit Cloud. CreditIQ is a loan default prediction system trained on 150,000 real borrower records. It goes beyond standard model comparison by implementing business cost threshold optimisation, finding the exact decision boundary that minimises total dollar loss for the lender rather than just maximising a statistical metric. Ensemble AUC-ROC of 0.844 with full SHAP explainability for every individual prediction. Also live on Streamlit Cloud. My third published project, a comparative study of OLS econometrics versus machine learning for Nigerian GDP forecasting, produced an 8x improvement in predictive accuracy using XGBoost over traditional regression and was submitted to the Journal of Economics and Business Review in 2025. What I bring is the combination of formal economics training and production Machine Learning engineering. An economics background means I understand the domain behind the data, why a variable matters, whether a finding makes economic sense, and when a statistically significant result is economically meaningless. That understanding shapes every modelling decision I make before writing a single line of code. I can support your project across the following: -- Data extraction, cleaning, matching, validation, and pipeline preparation -- Machine learning, classification, regression, clustering, anomaly detection, and predictive analytics -- Machine Learning Time series forecasting using ARIMA, GARCH, XGBoost, and ensemble methods --Machine Learning Financial analysis: credit risk modelling, and business cost optimisation -- Econometrics: panel data, causal inference, difference-in-differences, propensity score matching, and instrumental variables -- Statistical analysis and hypothesis testing using Python, R, and Stata -- Excel and Power BI dashboards and automated reporting for non-technical stakeholders -- NLP, sentiment analysis, text classification, and qualitative content analysis -- SHAP explainability and interpretable AI for regulated or presentation-facing outputs -- Machine Learning End-to-end deployment on Streamlit Cloud with live production systems Across all of this the standard is the same: clean documented work, honest evaluation, and outputs that hold up when someone asks hard questions about how the numbers were produced. My deliverables typically include organised datasets, clean and reproducible code, documented assumptions, validation checks, dashboards or reports, and a concise summary of the main findings. I communicate regularly, identify data limitations early, and focus on solutions that are accurate, practical, and easy to maintain. Send me your data or project requirements, and I’ll suggest the most effective approach.

I am a Statistical Consultant and MS Statistics student at UC Davis. I also received a B.S. in Applied Mathematics/Economics and a Minor in Business Analytics from the University of California, Riverside. 🏆 Elite Credibility: My coauthored research has been selected for presentation at premier national conferences, including: 1️⃣ 2025 Association for Public Policy Analysis & Management (APPAM), Seattle, Washington 2️⃣ 2025 Population Association of America (PAA), Washington, D.C. 📈 Specialized Statistical Services: I leverage R, Stata, and Python to solve complex causal and predictive problems: ✅ Descriptive Regression Analysis: Linear/nonlinear regression, probability modeling (LPM, logistic), ANOVA (One-Way, Two-way, Random / Fixed / Mixed Effects) ✅ Causal Inference: Difference-in-Differences (DiD), Instrumental Variables (IV), and Panel Data (Random / Fixed / Mixed Effects) ✅ Predictive Modeling: Machine Learning (Gradient Boosted Trees, Random Forest, KNN) ✅ Survey Methodology: Expert population weighting, clustering, and post-stratification ✅ Statistical Tests: A/B tests, T-tests, Chi-squared Tests, Hausman test, etc. Some Projects: 1️⃣ Github Website (with an interactive dashboard using PostgresSQL and Python, portfolio, and other webpages on reproducible research) 2️⃣ Regression Analysis of Airbnb prices in Mexico 3️⃣ Regression Analysis of H-1B sponsor rates by industry type and location In a year-long data analytics intern position, I: 1️⃣ Created and implemented a database into our center's initiative on analyzing academic technology usage. 2️⃣ Reached out to technology vendors of the university, requesting data extracts and communicating with them on the interpretation of data. 3️⃣ Streamlined data collection, and organized all data into a database that is still used at the center. 4️⃣ I cleaned and prepared several spreadsheets with hundreds of thousands of rows within Google Sheets, and provided key metrics through dashboards and reports in Google Looker Studio. 5️⃣ I connected this data and made it accessible through Slack. I reported on my progress through Slack, Asana, and Zoom meetings (disseminating findings through bi-weekly meetings). 6️⃣ I implemented data governance policies and facilitated meetings on data governance. 7️⃣ I wrote, recorded, and hosted trainings for my colleagues in Looker Studio. 8️⃣ Providing data-driven insights through story-telling. 🛠 Technical Toolkit: Languages: R, Python , SQL (PostgreSQL), Stata. Visualization: Plotly, ggplot, Looker Studio

I have extensive experience conducting analyses in R, SPSS, AMOS, JASP, and jamovi. I also use R for visualizations, and can make R Shiny apps. I am experienced with descriptive statistics, t-tests, correlations, regression (linear, multiple, logistic, lasso, etc.), ANOVA, mediation, moderation, principal components analysis, factor analysis, structural equation modeling, discriminant analysis. I also can create a wide range of figures (e.g., box plots, bar charts, heatmaps, histograms, scatter plots, biplots, etc.), depending on what is required. My interests and previous experience include -omics research (e.g., metabolomics, lipidomics, etc.), psychology, cognitive neuroscience (including a lot of EEG data), and sports analytics. I have a Ph.D. in psychology, and currently work in statistics.

⭐️⭐️⭐️⭐️⭐️ “Ishaq is a Professional Data Analyst and Research Consultant.” Python: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn, Plotly| R / RStudio, Rshiny | SPSS | STATA | EViews | Power BI | Tableau| MS Excel | Cloud Platforms: Microsoft Azure Machine Learning Studio, AWS SageMaker, and Google Cloud AI tools. 📊 Kindly Contact Me Before Placing Your Order! I am a Data Analyst, Machine Learning Engineer, and Statistician (PhD) with a strong background in transforming complex data into actionable insights and intelligent solutions. Over the years, I’ve worked across research, analytics, and AI-driven problem solving, helping businesses and researchers make confident, data-backed decisions. 🔹 What I Offer This gig provides comprehensive quantitative and qualitative data analysis with detailed, easy-to-understand reports using cutting-edge tools and technologies. 🔹 Areas of Expertise I specialize in: Data Analysis & Visualization: Python, Power BI, Tableau, MS Excel Machine Learning & AI: Scikit-learn, TensorFlow, PyTorch, NLP, Deep Learning, Predictive Modeling, and Time Series Forecasting Statistical Modeling: Hypothesis Testing, Regression, ANOVA, Bayesian Inference, and Experimental Design Data Engineering: SQL, ETL Pipelines, Data Cleaning, Preprocessing, and Automation Cloud-Based Analytics: Deploying ML models and data pipelines on Azure and AWS Research & Academic Writing: Statistical consulting, reproducible research, and technical documentation. I also conduct specialized analyses in: Biostatistics, Bioinformatics, and Biometrics Econometrics and Economics Environmental and Genetic Statistics Mathematical and Applied Statistics Time Series and Forecasting Hypothesis Testing (Chi-square, t-test, z-test, etc.). 🔹 Why Work With Me With PhD-level expertise in data science and statistics, I combine academic depth with industry-grade analytical skills. Every project is approached with precision, reproducibility, and practical insight, ensuring your data tells a story that truly matters. 🔹 What You Can Expect ✅ Accurate and reproducible analysis ✅ Clear interpretation and professional visualization of results ✅ Fast delivery and transparent communication ✅ 100% client satisfaction Let’s bring your data to life through intelligent analysis and modern AI solutions. Kindly contact me before placing your order to discuss your project requirements in detail.