Retail Predictive Analytics Data Scientist

@MashoodurRehman1

I am a data scientist with extensive experience in developing predictive analytics solutions specifically for retail. I have a strong background in handling diverse datasets, ranging from structured data such as sales transactions and product metadata to unstructured text, including customer reviews and social media. My expertise includes data ingestion, cleaning, feature engineering, and model deployment using Python libraries like pandas, scikit-learn, and TensorFlow, alongside NLP tools like spaCy and transformers. I have previously worked on projects that required forecasting demand at SKU and store levels, exactly what you're seeking. I am proficient in creating reproducible scripts and delivering clear documentation of assumptions and processes, ensuring the final model is both accurate and interpretable. My deliverables consistently align with the high standards you outlined, including thorough peer review processes for code quality. I am interested in further discussing how I can meet your specific requirements and deliver exceptional results. Could you please clarify your preference for timeline milestones? I am flexible and can accommodate your needs accordingly.

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@devendrathakur12

Hi, I’m a Senior Data Scientist with 20+ years in predictive analytics and machine learning. I have gone through your specific requirement for retail demand forecasting. I built something like this for a data client with 100K+ records, using Python, XGBoost and PostgreSQL. I would use XGBoost for the main forecast instead of Prophet, because SKU and store level drivers need richer features than time series alone. I will first profile the sales history and prevent future data leaking into the holdout set. Then I will combine structured features with review and social text signals using Python NLP pipelines. And I will validate the model against your MAPE target before packaging the prediction API. At least that is where I would start. I can send relevant notebooks and ML project samples. What SKU and store history is available, and at what frequency? How is the current holdout test set defined today? Which business actions matter most from the forecast, merchandising or marketing? Free for a quick call this week? Or answer those three and I’ll map the first version tonight. Dev Singh

@pkundu25

As a Senior Data Scientist with a proven track record in academic research and analytics, I am excited to deliver your end-to-end predictive solution. I specialize in fusing structured transaction tables with unstructured text to unlock hidden value and predictive signals. Technical Approach & Stack: - Data Pipeline: SQL for robust data warehousing; Python (Pandas) for cleaning, handling missing values, and data ingestion. - NLP Engine: Transformers/spaCy to extract sentiment scores and trend signals from customer reviews and social media chatter, feeding them directly into the demand models as features. - Forecasting Engine: An ensemble approach combining Prophet (for baseline macro-trends/seasonality) and XGBoost/LightGBM (for complex, cross-feature interactions like marketing logs and pricing) to consistently beat your target MAPE < 10%. - Deployment: Production-ready Python API (FastAPI) containerised via Docker for seamless, reproducible deployment. Milestones & Timeline: 1. Data Prep & NLP (W1-2): Ingestion, cleaning, sentiment feature engineering. Deliverable: Cleaned datasets & EDA notebook. 2. Modelling & Ensemble (W3-4): SKU/store level demand forecasting and tuning. Deliverable: Validated models meeting metrics. 3. Deployment & Final Delivery (W5): API packaging, summary report, and executive slide deck. Deliverable: Production code + insights. Let's connect to discuss it further. I am ready to start the project work immediately.

@tangramua

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@maxscender

I’ll treat this as an end-to-end forecasting and insight problem, not just a modeling exercise. I’ll start by profiling and reconciling the structured and unstructured data to understand coverage, quality, and the true drivers of sell-through, then engineer features that reflect product, store, promotion, seasonality, and sentiment effects. For demand forecasting, I’ll build a validated ensemble approach—likely gradient boosting for SKU-store level patterns with a time-series component for trend and seasonality—and I’ll keep the feature set interpretable so merchandising and marketing teams can act on the why, not just the what. The text data will be used to extract sentiment and emerging topics, then folded into the demand model where they show predictive lift. I’ll structure the code so the pipeline runs reproducibly from raw data to deployment, with clear assumptions, documented feature definitions, and a clean API wrapper for ongoing predictions. The final deliverable will include validated performance metrics against your MAPE target, plus a concise summary that translates the model outputs into concrete retail actions.

@vishal1145

Your demand forecast will drift if you don't segment by store cluster and seasonality pattern upfront. Most retail models fail because they treat all SKUs identically, ignoring local demand shocks and promotional lift decay. Quick questions - are you capturing markdown events and competitor pricing in your campaign logs? And do you have historical stockout flags, because missing those creates survivorship bias in your training data? Here is the architectural approach: - PYTHON + XGBOOST: Build hierarchical forecasting pipeline with store-cluster embeddings, lagged features for promotional lift, and Prophet decomposition for trend-seasonality separation. - NLP ON REVIEWS + SOCIAL: Extract sentiment scores and topic clusters using transformers, then engineer rolling sentiment features that lead demand shifts by 7-14 days. - DEPLOYMENT-READY API: Package the ensemble in FastAPI with automated retraining triggers when MAPE degrades beyond threshold, plus Grafana dashboards for merchandising teams. I've built similar demand-forecasting systems for two CPG brands that reduced overstock by 22% while maintaining 98% in-stock rates. Let's schedule a 20-minute call to align on your feature availability and deployment infrastructure before I draft the milestone roadmap.

@SkillCrafts

I understand you're seeking a data scientist to build a robust predictive analytics solution for retail, similar to how I've leveraged unstructured text data (customer reviews, social media) to identify key drivers and forecast demand in past projects, enabling actionable merchandising and marketing strategies. My approach would involve an end-to-end workflow: initial data exploration and cleaning using Python (Pandas, NumPy) and SQL for structured data. For unstructured text, I'll employ NLP techniques like TF-IDF, sentiment analysis, and topic modeling (NLTK, spaCy, scikit-learn) to extract meaningful features. Demand forecasting will utilize time-series models (ARIMA, Prophet) and potentially machine learning regressors (XGBoost, LightGBM) trained on aggregated features. Model evaluation and selection will prioritize interpretability and robustness. Deployment will be containerized (Docker) with clear documentation and version control. To ensure alignment, could you elaborate on the primary business objective driving this project – is it optimizing inventory, personalizing promotions, or something else? And what are your current expectations regarding model refresh frequency and the desired output format for insights? I'm eager to discuss how my skills can directly address your needs.

@carlosd178

Hello, As a result of a detailed review of your project requirements, I fully understand the scope and expectations. I have experience with Python, SQL, retail forecasting, feature engineering, predictive modelling, and NLP pipelines for mixed structured/unstructured datasets. In my opinion, the key challenge is combining transactional, product, campaign, review, and social signals without introducing leakage while still producing an interpretable SKU/store-level forecast. I would structure the work in milestones: data ingestion/cleaning → EDA and feature engineering → baseline forecasting → XGBoost/ensemble modelling → NLP sentiment/topic features → validation and error analysis → deployment/API and documentation. I’d use time-aware validation, hierarchical SKU/store features, promotions/seasonality, lag/rolling variables, and SHAP or similar methods for interpretability. I can target MAPE <10%, but I’d first validate whether that threshold is realistic for all SKU/store segments and report performance by category rather than hide weak areas. A couple of quick questions: • How much historical sales data is available? • Do you need daily, weekly, or monthly forecasts? Best regards, Carlos

@devanshkhandekar

Hi,I am a seasoned Applied ML/Data Scientist(6+ yoe) experienced in production predictive analytics,transactional modelling,anomaly detection,SQL pipelines & deployable ML APIs -Built an end-to-end financial fraud/anomaly engine combining XGBoost with Isolation Forest,using behavioral features such as transaction velocity,amount-vs-user baseline,new-device/location activity & temporal spending patterns;SHAP provided transaction-level explanation -A niche challenge was temporal leakage: rolling behavioral features must contain only information available before prediction time.I’ll apply the same discipline to SKU/store demand forecasting so future campaign/sales signals cannot inflate hold-out performance -For your retail system,I’ll unify transactions,product attributes & campaigns in SQL,then derive lag sales,rolling velocity,promo lift,price effects,store/SKU seasonality & stockout-aware features -Reviews will become transformer-based sentiment/topic embeddings aggregated by SKU & time window,with lag testing to determine whether they genuinely lead demand -I’ll benchmark seasonal baselines against XGBoost/LightGBM & time-series ensembles,using rolling-origin validation & MAPE/WAPE/bias diagnostics -Critical issue: observed sales can be censored by stockouts; blindly treating zero sales as zero demand produces systematically under-forecasted replenishment -Delivery: reproducible pipelines,SHAP insights,FastAPI prediction service,Docker environment & reports

@abdf2010

Hi, I am a professional web developer and I can do this project "Retail Predictive Analytics Data Scientist", I have 5 years of experience in web development. I have done many projects like this. I can do this job for you. I can start right now. Please contact me. Thanks

@robertb297

Hello. Your biggest challenge here is not simply forecasting sales—it’s combining transaction, product, campaign, review, and social data without introducing leakage while still achieving the <10% MAPE target. I can build the pipeline end-to-end: data cleaning and SQL preparation, SKU/store-level demand forecasting, NLP features from reviews and social data, model benchmarking, and final deployment/API. I’ll prioritize interpretable features so the output is useful for merchandising and marketing decisions, not just a high-performing model. Proposed milestones: 1. Data audit, cleaning & EDA 2. Feature engineering & baseline models 3. XGBoost/forecasting model development and validation 4. Error analysis and optimization toward <10% MAPE 5. Deployment, documentation, report and slide deck You’ll receive reproducible Python/SQL code, validated metrics, requirements/environment files, and a deployment-ready prediction pipeline. I’m ready to review the datasets and existing data structure to define the best forecasting approach before modelling.

@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings

@MuhammadUzair16

Hi, I am a software engineer with over 16 years of experience building data-driven systems, including Python pipelines, forecasting models, NLP workflows, and deployment-ready APIs. I can turn your transaction, product, campaign, review, and social data into a reproducible retail analytics solution that connects demand forecasts with practical merchandising and marketing actions. My approach will follow four milestones: first, data ingestion, quality checks, EDA, and a documented forecasting baseline; second, SKU/store feature engineering plus sentiment and topic signals from text; third, time-aware model training, ensemble comparison, error analysis, and hold-out validation against the MAPE target; and fourth, modular deployment code/API, environment setup, report, and slide deck. I will also check performance by SKU/store segments so a strong aggregate score does not hide weak areas. How much historical data is available, and what is the required forecast horizon? Also, where should the final prediction service be deployed? I would be glad to discuss the data structure, milestones, and accuracy expectations in detail.

@Abror86

Hi-Abror Here From Uzbekistan. "Retail Demand Prediction And Insights" - I can build an interpretable predictive pipeline using Python, XGBoost, and NLP for reliable retail forecasting. I can clean and integrate transactions, product, marketing, review, and social data while engineering features and validating SKU-store demand models. I will deliver reproducible notebooks, validated metrics, business insights, deployment-ready prediction code, documentation, and a modular environment setup for handover. Which forecasting horizon and SKU-store granularity should the model prioritize first? Looking forward to working with you.

@engruhulajom

Hello Dear! Good Day! Hope you are doing fine. This is Ruhul Ajom Sagor. I am an expert "Web Developer" with 10+ years of working experience in PHP, HTML5, CSS3, JavaScript, jQuery, Bootstrap, MySql and different Frameworks. I have completed my B.S.C Engineering in Computer Science and Engineering (CSE) from BUET. Hire me and you don't have to worry about your website problems again! I'll add value to your projects by creating astonishing designs and code with high impact and optimized user interaction that leads to bigger conversions. WHAT PROBLEMS CAN I HELP YOU SOLVE? • Custom Websites Using PHP and Frameworks • e-Commerce Websites (Woo-Commerce and Shopify) • Custom WordPress themes • On-Page and Off-Page SEO • WordPress themes Customization • Database Modeling/Development • WordPress migrations and upgrades • Responsive Coding (Make your website compatible with: smartphones, tablets, desktops) • Websites speed and loading time improvements • Cross-browser compatibility • PSD to HTML to WordPress conversion • HTML5/CSS3/jQuery websites based on Bootstrap I love challenges, talking to my clients, and meeting others’ standards as well as expectations. I will be discussing everything in detail, giving my full advice and delivering through best of my skills. You are cordially welcome to discuss your project. Thank You! Best Regards, Ruhul Ajom

@rohank2502

You need sell-through drivers, SKU and store demand forecasts, and actions merchandising and marketing can actually use. I can start right now. In 24-48 hours you get a live sample on your data shape: cleaned tables, a first forecast you can read, and a short note on what moved it. I ship production systems weekly. I will not claim a retail forecast I have not built for you. You see your sample first, then we lock the cleaned data, the trusted forecast, and a clear summary pack. Which slice should the sample use, one store and its top SKUs, or a few products you care about most?

@invoketech

Hello, I understand you need a retail predictive analytics solution that combines sales, product, marketing, customer reviews and social data to forecast SKU/store demand and turn the results into actionable merchandising insights. I can handle the complete workflow: • Data ingestion, cleaning and EDA • SKU/store-level demand forecasting • Feature engineering from promotions, seasonality and product attributes • NLP sentiment/topic features from reviews and social chatter • XGBoost/Prophet/scikit-learn or ensemble modeling based on validation results • MAPE, MAE and other hold-out performance metrics • Reproducible notebooks, modular Python scripts and SQL • Deployment-ready prediction API/code • Business-focused report and presentation Workflow: Data ingestion → cleaning/EDA → feature engineering → forecasting/modeling → validation/backtesting → deployment → business insights. I’ll specifically work toward the <10% MAPE target using proper time-based validation rather than data leakage, and provide requirements/environment files so the solution is reproducible. I have strong Python, ML, NLP, SQL and predictive analytics experience and can begin with data profiling and baseline forecasting before selecting the final model. Thanks

@emrahg7

Hi, Picture this: you pull up a dashboard and it tells you which SKUs at which stores are about to run hot or cold, backed by numbers you can actually defend to your merchandising team. That's the end state here, and it's a build I've done before, matching sales history with review text and social chatter to catch demand shifts before they show up in the numbers alone. My approach: clean and join your structured tables first, get a solid baseline forecast running with XGBoost or Prophet at SKU/store level, then layer in NLP on reviews and social posts to pull sentiment and topic signals as extra features. The part I'd watch closely is data drift, retail patterns shift fast with promotions and seasonality, so I'd build in a retraining check rather than a one-and-done model. I'll break this into milestones like you asked: data prep and EDA, baseline model, NLP feature layer, validation against your MAPE target, then packaging into a deployable endpoint with docs. Quick question before I quote milestones in detail: how far back does your sales history go, since that changes how much seasonality the model can actually learn. I can start on data prep this week. Best, Emrah