Position: Data Scientist
Company: Creative Advanced Technologies
Category: Data Science
Experience required: 3y
English level: intermediate

Such algorithms include developing models for product search, recommendation systems (e.g., ranking, page generation), personalization (e.g., evidence, messaging, marketing), media assets generation and optimizing current systems. We are looking for experienced Data Scientists with a specialization in Natural Language Processing (NLP) to building a center of excellence for machine learning and analytics. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. In this role, you will participate with a group of engineers to design, train and deploy ML models. Also, you will closely collaborate with other engineering teams (including Hardware, QA, Infrastructure) to ensure the solutions are meeting the highest quality standards. 
As a Data Science Engineer at CreativeAdvTech, you will employ techniques and theories drawn from areas of mathematics, statistics, semiconductor physics, materials science, and information technology to uncover patterns in a large amount of data from which predictive models, actionable insights, and solutions can be developed. You will interact with experienced Data Scientists, Data Engineers, Business Areas Engineers, and UX teams to identify questions and issues for data analysis projects and improvement of existing tools. As a member of the team, you will develop innovative and sophisticated solutions to some of the most complex strategic business challenges. To be successful in this role, you need a strong machine learning background, solid software development skills, a love of learning, and to collaborate well in multi-disciplinary teams. You will need to exhibit strong communication and leadership skills, an ability to set priorities, and an execution focus in a dynamic environment. 

**Responsibilities:**

⃟Ability to research, design, and develop new machine learning solutions and algorithms.
⃟The experience to drive high level algorithm decisions to ensure fast and accurate machine learning in a multitude of different applications
⃟Implement cutting edge machine learning techniques in object classification, labelling, pose estimation, and prediction
⃟Use real-time state estimation and context from surroundings to predict the motion of surrounding vehicles and pedestrians for use with vehicle path planning
⃟Work on data association, clustering, segmentation, filtering, and estimation
⃟Build robust, reliable systems to handle common and uncommon situations on the road		

**Skills and Experience:**

⃟MS/Ph.D. in CS/CE/EE, or equivalent industry experience.
⃟3+ years of experience as a machine learning engineer
⃟Excellent mathematical reasoning skills, especially with probability.
⃟Experience in the areas: statistical modeling, feature extraction, and analysis, supervised/unsupervised/semi-supervised learning. Exposure to the semiconductor industry is a plus but not a requirement.
⃟Ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.
⃟Experience in Recommendation Systems, Personalization, Search, Computational Advertising or Natural Language Processing
⃟Strong programming skills in Java, C/C++, Python, or similar language
⃟Experience developing enterprise production machine learning models.
⃟Experience with Cloud platforms such as AWS, Azure, Google Cloud Platform (GCP).
⃟Excellent problem solving and analytical skills
⃟Excellent communication and collaboration skills with strong verbal and written skills.
⃟Passion for delivering business impact
⃟Experience with Spark, TensorFlow, Keras, and PyTorch.
⃟Track record of driving ML research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization.

**Good to have:**
⃟Experience in trust and safety machine learning models. 
⃟Existing papers in top conferences.