Data Scientist Resume Examples
- Scraped data from real estates sources
- Built outlier detection, clustering models
- Created model for buy/rent price prediction
- Prepared R shiny web application for model usage
- Handled design, modelling and deployment for a NLP project to automate resume parsing.
- Developed case study on Attention Models and SOTA architecture BERT
- Developed various Machine Learning, Deep Learning and Artificial Intelligence activities as part of the course material
- Taught lectures to nearly 100 professionals in the range 0-25 years of experience.
- Used data mining and data cleaning techniques to analyze job posting descriptions
- Used Python to parse keywords from a self-created dictionary to determine specific trends in Computer Science jobs such as popular programming languages, more need for soft skills, etc.
- Performed Machine Learning algorithms (Linear Regression, Clusters) to determine future trends in the field
- Experienced using many Python Data Science and Machine Learning libraries such as Pandas, Numpys, Seaborn, Scikit Learn and Matplotlib
- Build Attribution and Prediction Models using Time Series , Regression and other Machine Learning techniques to support the IB research analysts
- Build R Shiny applications- For Campaign Analytics, Data Quality Monitoring – which results into efficient functioning and time saving
- Survey Analysis using bespoke analysis methodologies
- Optum Deep Vision(Intelligent Character Recognition):
data scientist (stage)
- Etude des mécanismes de transparence publicitaire de Facebook et de Twitter.
- Développement d’une plateforme pour l’audit des annonces publicitaires fournis par les médias sociaux.
- Développement d’un outil de visualisation des données et Analyse statistique des annonces
- Platform lead for 3 of the company’s software development projects involving SQL Server, MySQL and ParAccel (Actian Matrix)
- Built Python Web Service in Flask API to run the models. It helped in running the model on production.
- Maintained and updated model which used to calculate the Optimal Price at which IBM products should be sold to the client and calculates the Probability of Winning the Bid.
- Effectively communicated analytical results to key stakeholders using strong data visualizations, superior presentation skills and business language to emphasize the so what of the analysis.
- Applied statistical and algebraic techniques to interpret key points from gathered data.
- Coached, developed and motivated team members, providing coaching and mentoring to junior data scientists on Python and data mining techniques.
junior data scientist
- Migrated the Business Intelligence application on Django to vue.js. Also set up to work as progressive web app.
- Configured the AWS SageMaker to build an organized and efficient model environment. As well as developed some Linear Regression, Kmeans and Gradient Boosting models.
- Worked on Faviely doors to improve the availability of the system by predicting the possible factors influencing the maintenance schedules.
- Working on HVAC systems to builld a predictive model to identify the Lockouts/failures to reduce the down time of the system.
- Worked in the R&D team.
- Developed Algorithms for predicting the early onset of disease based on various health vitals.
- Developed Algorithms for predicting risk score of patients
- Performed data analysis on health vitals to bring out the meaning full correlation between several parameters or factors.
- Working with CNC and P2P teams of Philips on developing a Classification based Cash Flow Forecasting Model for getting estimated incoming and outgoing cashflows beforehand. Successfully implemented the first phase of the project by designing an Invoice Collection Prediction Model using Random Forest Classifier
- Clustering,Survival Analysis and Time Series on are client data. Recommendation System on Nature Basket.
- Worked on Chat bot related to domain like – Product & Service,Tours & Travel and HR BOT.
- Descriptive analysis on Media data.
- Leading the area of data science in my section of the organization. Owning end to end responsibility for identifying opportunities to develop data science solutions, in order to improve production and solve problems.
- Designing and developing software to solve complex analysis problems, using machine learning and various statistical models.
- Designing and developing software tools for data analysts, to automate and optimize everyday analysis processes.
- Providing consultancy for data analysts in the organization. Applying innovative solutions to complex analytic problems.
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