Software Engineer

Description de l'offre

Following the steps of quantitative finance, online advertising is transitioning
from a marketing domain to a purely mathematical one with advertising
inventories being bought in real time through exchange markets by “Media Trading
Desks”.
Scibids Technology equips these trading desks with an artificial intelligence
software computing the optimal amount to bid for each advertising opportunity,
thus bringing the final advertisers dramatic performance and productivity
uplifts. The technology developed by Scibids is rooted in a solid scientific and
market expertise, with one of the founders holding a PhD in machine learning
from ENS Cachan and 8 engineers cumulating 30 years of experience in
quantitative advertising.

Our society was selected in 2017 as one of the 62 most innovative startups
(amongst 2000+) in the extremely selective “I-Lab” competition by the French
public bank & French research ministry. It was also selected in the "Top 100
French Startup To Follow in 2017" by Business Insider.

One year only after its creation, the company already has several dozens of
clients throughout the world and its portfolio keeps on growing!


The mission of Scibids is to provide media buyers with fully automated buying
strategies for display campaigns, leading to uncompared performances for a
minimum of human work.

As a Software Engineer, your role will be to design, develop and improve
Scibids’ software, which is the cornerstone of our product. This means
integrating Scibids technology in a large number of partner platforms, building
bridges between client needs and Scibids AI algorithms, and more generally
contributing to make Scibids the most efficient fully automated trading solution
in the market.
This position will combine high development standards, a relentless curiosity, a
strong understanding of business priorities and the ambition for your work to
become a real game changer!

On a day to day basis, your responsibilities will include:
* integrating Scibids technology in numerous partner platforms (data ingestion
as well as delivering Scibids buying strategies via API).
* optimizing the implementation of Scibids AI algorithms, including choosing
the optimal technology and architecture.
* developing web interfaces allowing clients to configure Scibids solution,
including developing the communication flow between these interfaces and
Scibids algorithms.

Profil recherché

Minimum qualifications: * Advanced knowledge of one or several programming
languages.
* SQL proficiency.
* Knowing how to program in a simple and short way (KISS principle).
* Interest and ability to learn other coding languages
as needed.


Preferred qualifications: * Professional experience in an industry programing
language (eg Python, C++...).
* Professional experience working with large datasets.
* Experience working with two of the following: web
application development, distributed and parallel systems,information
retrieval,machine learning, developing large software systems.
* Conducting a regular technology watch on
data-related subjects.


Cultural fit
* Do things only once. No repetitive task survives to ingeniosity and
automation: at Scibids, everything needs to scale!
* Base your decisions on data points. Preconceived personal convictions rarely
survive thorough confrontation with actual data.
* Work in team and be curious. Every Scibids member has stock-options and is
focused on the same objective: make Scibids the next adtech unicorn! This
means being interested in how one’s daily personal work fits in Scibids
general strategy and having in mind the greater good of the company rather
than individual accomplishments.
* Have a good laugh. We are a young, hard-working team with huge ambitions:
nothing wipes off long hours of work better than a good beer with the guys!

À propos de Scibids

Scibids offers to media buyers a next-generation bid engine based on
cutting-edge machine learning algorithms. Easily integrated in many DSPs (such
as AppNexus or MediaMath), it uses each and every information to determine the
most relevant price to bid in real-time one buying opportunity after another,
leading to significative performance uplifts.