Harbor Analytics needs a data pipeline for a logistics client: ingest daily sales exports (CSV over SFTP), load them into PostgreSQL, model the data and build a reporting dashboard the client's ops team can use.
The dashboard should cover revenue by lane, on-time delivery rates and weekly trends. We are open to your tooling suggestions for the dashboard layer as long as it is maintainable and self-hosted.
Documentation and a handover call are part of the scope. Data volumes are modest, low millions of rows.
Data & Analytics/Expert/12 days ago
Ops managers at the freight companies using our TMS keep asking for reporting we do not have, and exporting CSVs for them is eating our support team alive. We are FreightPath, a 12-person logistics SaaS, and we want embedded analytics built into our existing React app: on-time delivery trends, lane profitability, driver utilization, and carrier scorecards, all filterable by date range and customer.
The work splits into two halves. First, design the aggregate tables and materialized views in PostgreSQL so dashboard queries return in under two seconds against ~40M shipment rows. Second, build the dashboard views in React using our existing component library (we use Recharts already, no need to introduce anything new). We will provide designs for the first two dashboards; you will help shape the rest.
Our backend lead handles API plumbing, so you can stay focused on queries and UI. Code review turnaround is same-day. We would rather you ship one polished dashboard a week than four rough ones at the end.
Data AnalysisPostgreSQLReact
$4,000.00 - $6,000.00 fixed price0 bids
Data & Analytics/Intermediate/18 days ago
Retention on our language learning app looks fine in aggregate but we suspect it is hiding two very different stories: exam-preppers who churn the day after their test, and hobbyists who stick around for years. Before we redesign onboarding around the wrong persona, we want someone to actually segment the base and prove or disprove this.
You will get read access to our Postgres warehouse (event data for ~800k users over 3 years) and a list of the product questions we are wrestling with. Expected output: cohort curves cut by acquisition channel, stated goal, and first-week behavior; a survival analysis of the top segments; and a working session where you walk our product team through what the data supports and what it does not. SQL and Python notebooks stay with us.
We budget this at 10-15 hours a week for about three weeks. Our head of product is your main contact and responds fast. Curiosity about the why behind the numbers matters more here than fancy modeling.
Data AnalysisPostgreSQLPython
Data & Analytics/Entry/27 days ago
Honest description of the mess: our regional sales team has been logging deals in a mix of spreadsheets and an old CRM export since 2021, with inconsistent customer names, duplicate entries, and three different date formats. We distribute commercial kitchen equipment across the Midwest - about 11k deals total, so this is very fixable, just tedious.
The job is to dedupe and standardize the historical data into a clean PostgreSQL schema we host, document the cleaning rules you applied so we can trust the numbers, and set up a monthly reporting query pack: revenue by region and rep, top accounts, product mix, and year-over-year comparisons. Reports can be plain SQL outputs to CSV - we do not need dashboards yet.
Good first project with us if the trial goes well; we have a backlog of analytics wants. Clear written communication is essential because nobody on our team is technical.
Data AnalysisPostgreSQL
$700.00 - $1,300.00 fixed price0 bids
Data & Analytics/Expert/1 month ago
Our growth team at Repwise runs 4-6 experiments a month on our fitness app, but every analysis is a bespoke notebook and we have definitely shipped at least one winner that was actually noise. We need a statistician-engineer hybrid to build the framework that stops that: standardized metric definitions, correct handling of ratio metrics and pre-experiment bias, sequential testing or CUPED where it fits, and honest power calculations before we launch anything.
Deliverables: a Python analysis library with tests, a written decision playbook (when to call it, when to extend, when to declare inconclusive), and templates that turn a raw experiment readout into a decision memo. You will validate the framework by re-analyzing our last six experiments - we genuinely want to know which of our past "wins" survive scrutiny.
We are looking for someone who has done experimentation at scale and has opinions. A short paragraph in your proposal about a common A/B testing mistake you see teams make will get you to the top of our list.
Data AnalysisPythonMachine Learning
$2,500.00 - $4,200.00 fixed price0 bids