# Data Scientist at Ebiquity

> Hybrid · Bulgaria · Unspecified · Analytics / Data science · open. Source: https://alderhq.app/jobs/pinpoint%3Aebiquity%3A565783 (read 2026-11-06).

## Facts (observed)

- Company: [Ebiquity](https://alderhq.app/companies/ebiquity)
- Location text: Bulgaria
- Where it can be done from (inferred): Hybrid · Bulgaria (evidence: "Location: Bulgaria")
- Posted: already open when tracking began; date unknown
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://ebiquity.pinpointhq.com/en/postings/bf303ca0-b2dc-4d47-bf28-aca57754e337
- Skills listed (inferred): Python, AWS, GCP, Azure, SQL

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

You will join our Data Science team and help turn client data into reliable models and reusable analytics tooling. This is a hands-on modelling and engineering role: you will spend your time on data, code, and statistical methods.

### Responsibilities

- Prepare, clean, and quality-check datasets using SQL and Python or R
- Build and validate statistical models, including regression, machine learning, and forecasting
- Apply sound statistical thinking, including a working understanding of Bayesian ideas (priors, uncertainty, posterior interpretation)
- Automate recurring analysis so the team can reuse pipelines, functions, and tools
- Use Git to work with the team’s codebase
- Document methods and results clearly so others can review, rerun, and present the work
### Skills

- 1–2 years in an Analytics or Data Science team
- Degree in Computer Science, Data Science, Statistics, Mathematics, Economics, or a related quantitative field
- Strong coding in Python or R (both is a plus)
- SQL
- Solid statistics: regression, machine learning fundamentals, and model validation
- Working understanding of Bayesian methodologies
- Comfortable with Git
- Fluent English, written and spoken
- Intellectually curious, organised, and able to work independently on well-scoped problems
Nice to have

- Hands-on Bayesian tooling (Stan, PyMC, brms, or similar)
- Cloud familiarity (AWS, Azure, or GCP)
- Experience in media, marketing, or marketing-effectiveness work
- Time series, causal inference, or test design

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/pinpoint%3Aebiquity%3A565783 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.