Data Science Engineer

3 semanas atrás

São Paulo, Brasil KPIT Tempo inteiro

We KPIT ( are a global technologies company specializing in CASE (Connected, Autonomous, Shared, Electric) domains. Systems and Software in Electric & Conventional Powertrain, Autonomous Driving & ADAS, Digital Connected Solutions, Connected Vehicles, Vehicle Diagnostics, and Vehicle Networks.


Join the leading software development and integration team helping mobility leapfrog towards a clean, smart, and safe future. A company specializing in embedded software, AI, and digital solutions, KPIT accelerates clients’ implementation of next-generation technologies.


**RESUME MUST BE SUBMITTED IN ENGLISH**


Responsibilities:

  • Smart, Versatile, Dynamic and Solution oriented engineers for building analytics using AI/ML involves using advanced data analysis techniques, machine learning algorithms, and artificial intelligence technologies to build predictive and prescriptive analytic models. In this role the engineers would be involved in creating exclusive IP solutions by covering all aspects of design, implementation, and validation. The engineers are expected to work with a global team to build in-vehicle network solutions for Automotive OEMs and their suppliers.
  • Data sourcing and preparation: Identify relevant data sources, gather and clean data, and transform it into a structured and usable format for analysis.
  • Model development and implementation: Develop AI and machine learning models using appropriate algorithms such as regression, classification, clustering, deep learning, or reinforcement learning. Implement these models into production systems for real-time analysis and decision-making.
  • Feature engineering: Identify and engineer meaningful features from data that capture the relevant information needed for the analysis. This may involve creating new variables, aggregating data, or selecting the most important features using techniques like principal component analysis or feature selection algorithms.
  • Experimentation and evaluation: Design and conduct experiments to evaluate the performance and effectiveness of different models and algorithms. Use appropriate evaluation metrics to assess model accuracy, precision, recall, or any other relevant performance measures.
  • Model validation and deployment: Validate the developed models using testing data and ensure they meet desired performance goals. Create documentation and guidelines for deploying and using the models in production environments.
  • Continuous improvement and optimization: Continuously monitor and optimize the performance of the analytical models. Analyse model outputs and feedback and iterate on the models based on new data and insights. Implement techniques such as hyperparameter tuning, ensemble methods, or feature selection to improve model performance.
  • Collaboration and communication: Collaborate with cross-functional teams, including data scientists, data engineers, domain experts, and stakeholders, to understand business requirements and ensure that the analytical models align with the organization's goals. Clearly communicate complex concepts and findings to both technical and non-technical audiences.
  • Data visualization and reporting: Use data visualization tools and techniques to present analysis results in a clear and meaningful way. Develop reports and dashboards that provide insights and recommendations based on the analysis.
  • Deep learning: Familiarity with deep learning frameworks such as TensorFlow or PyTorch to build and train neural networks. Deep learning techniques are particularly useful for handling complex data types such as images, audio, text, or time series.
  • In summary, a data science engineer in building analytics using AI/ML needs a mix of technical skills including data manipulation, machine learning algorithms, statistical analysis, data visualization, feature engineering, deep learning, model evaluation, software engineering, problem-solving, communication, and collaboration skills.


Requirements:

  • Data sourcing.
  • Develop AI , Knowledge & Some experience on AI/ML tools & Algorithm.
  • Python, Any statical programing language, SQL, Tableau, BigQuery, C++ Programming, Linux, Visual Studio, GIT.
  • Excellent knowledge of OS coding techniques, IP protocols, interfaces, and hardware subsystems.
  • Knowledge & Some experience on AI/ML tools & Algorithm.
  • Build analytics using AI/ML needs a mix of technical skills including data manipulation, machine learning algorithms, statistical analysis, data visualization, feature engineering, deep learning, model evaluation, software engineering, problem-solving, communication, and collaboration skills.
  • TensorFlow, PyTorch to build and train neural networks, Model validation.
  • Good understanding of data structures and algorithms.
  • Understanding of OOPs concepts.
  • Linux system software knowledge.
  • Knowledge of multi-threaded programming and associated constructs.
  • Knowledge of version control software development and management such as git or gitlab or github.
  • Knowledge of SDLC phases.

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