Predictive Analytics Jobs

I need to promote new film the 1st documentary all Christ teaching "The 40 Parables" see here I am starting a global B2B outreach starting in USA from Amazon SES and need an AI specialist who has already proved they can benifit SES in many ways from keeping large-scale campaigns out of spam folders and into decision-makers’ inboxes. My immediate priority is email delivery optimization—our small beta sent 50,000 through SES saw roughly 50%+ opens and 40%+ clicks but 0001% conversions, so I know the messages are reaching eyes; they are just not converting, and I want to multiply volume without multiplying problems. Because I am new to B2B email (I’m a 72-year-old documentary producer expanding into sponsorship and distribution partnerships) seeking AI-guided f...

Here’s what I need built: • A predictive-pricing engine that ingests lot attributes (grade, shelf-life, volume, historical demand) and recommends starting prices in real time. • An automated-bidding module so buyers can set ceilings and let the system compete for them. • A fraud-detection layer that flags suspicious listings or bidding patterns before a transaction closes. Everything must sit behind a clean, minimal interface—no clutter—because both food and pharmaceutical professionals will be using it every day under time pressure. I’m open on stack, but Python with TensorFlow/PyTorch for the models and a lightweight React or Vue front end makes sense; CUDA optimisation is a plus for the Inception pitch. Acceptance criteria for the MVP: 1. End...

Our company has amassed a rich set of customer data from CRM, support tickets, and usage logs, and I need a full prescriptive analysis that pinpoints actions we can take to boost retention. The work goes beyond describing what has happened; I need to understand why customers leave, simulate what-if scenarios, and receive clear, prioritized recommendations I can implement right away. You will have direct access to anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. I expect you to apply statistical modeling or machine-learning techniques of your choice, validate findings rigorously, and convert them into concrete retention strategies—loyalty offers, upsell timing, personalized messaging, or workflow changes—complete with expected impact. D...

Our company has amassed a rich set of customer data from CRM, support tickets, and usage logs, and I need a full prescriptive analysis that pinpoints actions we can take to boost retention. The work goes beyond describing what has happened; I need to understand why customers leave, simulate what-if scenarios, and receive clear, prioritized recommendations I can implement right away. You will have direct access to anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. I expect you to apply statistical modeling or machine-learning techniques of your choice, validate findings rigorously, and convert them into concrete retention strategies—loyalty offers, upsell timing, personalized messaging, or workflow changes—complete with expected impact. D...

I have a collection of patient medical records already cleaned and stored as CSV files, and I’m ready to turn them into an accurate diabetes-risk prediction tool. My preference is to work with a Random Forest model because of its balance between interpretability and performance on tabular health data. Your task is to take these CSV datasets, craft the full machine-learning pipeline, and return a model that can reliably identify individuals at elevated risk for diabetes. Along the way, please document the preprocessing steps, feature engineering choices, and the metrics you use so I can reproduce and fine-tune the work later if needed. Deliverables I expect: • Complete, well-commented Python code (preferably in a Jupyter notebook) that ingests the CSV files, performs prep...

I need a digital twin that zeroes-in on my building’s energy performance. Right now I only have partial data—BMS exports, sub-metering on a handful of panels, and monthly utility bills—so the model must be able to start with what’s available, validate any gaps, then scale as more sensors come online. Primary improvement targets: • Heating and cooling systems • Overall energy consumption The twin should connect to the existing BIM (Revit) files, ingest the live and historical data, and run calibrated simulations—EnergyPlus or an equivalent physics-based engine is fine—so I can visualise real-time KPIs and generate “what-if” scenarios for retrofit ideas. I’m interested in clear, actionable outputs such as predicted savings...