Fast Data Science

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Overview

$1,000+

Min Project Size

150+

Avg Hourly Rate

2 - 9

Employees

2018

Year Founded

ABOUT FAST DATA SCIENCE

Data Science Consulting

Consulting services focused on text, images, unstructured data, and the healthcare/pharmaceutical sectors. Microsoft-certified as an Azure Data Scientist.

LANGUAGES SPOKEN

English

HIGHLIGHTS

$1,000+

Min Project Size

150+

Avg Hourly Rate

2 - 9

Employees

2018

Year Founded

Services

Focus

Industries

Clients

Services Provided

60% Generative AI

40% BI & Big Data Consulting & SI

Portfolio

Predicting employee turnover

Unknown

A machine learning model designed to forecast employee turnover in an organization with 3 million staff members.

Finding molecules and proteins in scientific literature

Unknown

I have participated in various projects where clients required the analysis of scientific literature to track mentions of molecules or proteins. For instance, the molecule depicted on the right is Aspirin, which remains a trademark of Bayer in certain countries. In academic papers, it may be referenced as acetylsalicylic acid, 2-acetoxybenzenecarboxylic acid, C9H8O4, or by different identifiers like DB00945. Additionally, there may be identifiers related to other molecules or specific variations of a molecule. A frequently observed example in clinical literature is the ERBB2 gene, which plays a crucial role in specific breast cancer types. ERBB2 is also known as Erb-B2 Receptor Tyrosine Kinase, HER2, HER-2, among other designations, many of which also denote the protein produced by the gene. Many of these names resemble common English words and may not always be capitalized in text. Due to these complexities, the task of recognizing names of proteins, genes, and molecules in scientific literature presents significant challenges. I have developed several effective techniques to resolve these ambiguities. Typically, I require a set of annotated examples as a starting point, from which I train a machine learning model to learn and annotate incoming publications. This model can be implemented on the client’s servers, providing daily updates via a dashboard. This enables clients to track literature in real time concerning specific molecules, proteins, or genes and to identify trends proactively.

Predicting customer orders

Unknown

A retail client managed a fleet of vehicles that delivered produce at various times throughout the day. They utilized third-party logistics software to organize the delivery schedules, but determining the unloading time upon arrival at the store was challenging. Fortunately, there was a system in place that recorded vehicle ignition events, GPS locations, and geofencing to track when delivery vehicles arrived and departed. Additionally, historical schedules provided data on the types and quantities of products delivered, the drivers responsible, and the time of day as well as the types of vehicles used. By leveraging this extensive dataset, I developed a straightforward regression model to forecast unloading times for future deliveries during the scheduling process. This innovation enabled the client to reduce costs related to driver overtime, minimize disruptions from late deliveries, and avoid fines associated with drivers exceeding their legally allowed working hours.

1 more project

Projects & Clients

Project Size & Rate

Minimum Project Size

$1,000+

Average Hourly Rate

150+

Pricing Snapshot

Min Project Size

$1,000+

Avg Hourly Rate

150+

Cost Rating

N/A

What Clients Have Said

“

"Fast Data Science delivers quality projects within budget and timeline expectations based on verified client feedback."

Based on verified reviews and internal benchmark data

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Generative AI

BI & Big Data Consulting & SI

Locations

Headquarters

55 Sotheby Road

London, England, United Kingdom N5 2UP

+447906331524

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