Ishmael Sesay

Skills: Python · SQL · Business Analysis · Étude des séries temporelles · Analyse prédictive · Tableau · Microsoft Power BI

Work as a senior data consultant for various clients in order to accompany them in the resolution of data driven problems. Work with the business as well as technical teams in order to convert business problems into technical. • Data exploration & Data wrangling • Insight searching • KPI and dashboard creation • Anomaly detection in timeseries using Machine Learning models Notable clients: BNP Paribas, Decathlon, Pernod-Ricard

Co-creator of the Chanel WFJ Scanner for the entity Jewelery. • Creation of a Watch&Ring Scanner allowing every on site Chanel seller to have an in-depth expertise and level of detail on the scanned item in order to advise optimally every client. • Computer Vision tasks: Object-detection of a Chanel ring or watch, recognition of the item and then Object-Classification of the item within the existing series and références of the given item. • Technical stack : Python, Tensorflow, Pillow, Azure Devops, Azure Machine Learning. • Achievements: Launch and deployment of the WFJ scanner worldwide (US, UK, EMEA, APAC, JAPAN) More than 1 000 watchs & rings scanned.

• Management of 30 students in order to train them into becoming Data analysts • Creation of Data -related content (statistics, mathematics, propability, live codings, algorithmics exercices) • Gathering and assessment of Data projects with private clients, checking the relevance of the business problem in a Data-driven approach. • Help in the creation of an E-health Hackathon with Doctolib and Dataiku. Biggest online healthcare Hackathon in the European Union.

- Data Scientist in Jems Group, a tech consulting firm established in Neuilly-sur-Seine. - Creation of a predictive model for Poclain Hydraulics, using decision trees algorithm and ensembling methods in order to predict the needed stock items for a given period and detecting the evolution of stock market indexes based on relevant open data features such as GDP per capita or gaz prices, collected on reliable sources such as World Bank. - Creation of a predictive model using the XGBoost algorithm for Poclain Hydraulics in order to discriminate functional motors from dysfuncional ones based on their performance recordings.

- Work with Mana Community, a client which is an international NGO whose aim is to detect environmental hazards linked to the activites of big corporations. Their goal is to shed light on tangible data coming from reliable sources, process them, and create relevant indicators (KPIs) • IBM Watson Machine Learning. • IBM Watson Natural Language Understanding (NLP). • Python (+DataScience libs, sk-learn, numpy, pandas, matplotlib). • Apache Spark.

Research work with the CENS team from the SANPSY Laboratory, under the supervision of Dr Ellemarije Altena. The goal was to investigate emotional reactivity as measured by facial expressions in response to emotional stimuli in patients suffering from primary insomnia and in control patients. • Inclusion/exclusion of patients. • Setting up of the facial recognition task. • Processing of patients' data. • Analysis of patients' data using statistical tests. • Use of driving and flying simulators in order to study how the symptoms are expressed in given conditions. • Use of virtual reality to palliate patients' phobias and symptoms by means of immersive new technologies. • Use of artificial intelligence to create a convenient and optimal response to the patients' needs.

Internship in a Neuroscience laboratory at the Inserm U862 specialised in the physiopathology of neural plasticity,in order to understand and study cases of multiple sclerosis. • Study the symptoms on animal models such as mice. • Highlighting the alteration of the myelin of neurons. • Quantification of altered neurons. • Processing and analysis of the patients' data