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Data Scientist Job at Impetus: https://www.linkedin.com/jobs/view/4203764847

Actimise hiring Data Scientist

Location: Pune, India

Apply link: https://job-boards.eu.greenhouse.io/nice/jobs/4567868101?gh_jid=4567868101&gh_src=ff5c4b52teu

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©How fresher can get a job as a data scientist?©

1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills.

2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization.

3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs.

4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections.

5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science.

6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science.

7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field.

ML Engineer - Jaipur
Product Organisation - Jaipur, Rajasthan (Hybrid)

https://peak.bamboohr.com/careers/370

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Blue Matter is hiring Analytics Associate 🚀

Min. Experience : 6 Months
Location : Gurugram

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https://jobs.careers.microsoft.com/global/en/job/1818159/Data-Scientist

Microsoft Hiring for Data Scientist Roles in US for all levels and locations!

Zycus is hiring AI Engineer

For 2021, 2022, 2023, 2024 gards
Location: Multiple

https://zycus.sensehq.com/careers/jobs/56968

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

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Airtel is hiring Growth Analyst 🚀

Experience : 1+ Year
Location : Gurugram

Apply link : Check out this job at airtel: https://www.linkedin.com/jobs/view/4206005779

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Company Name: Zycus
Job Role: AI Engineer
Location: Mumbai, Pune, Bengaluru

Apply Link: https://zycus.sensehq.com/careers/jobs/56968

Limited Time Opportunities.
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Thank You ❤️

Atlassian hiring Senior Data Scientist - Machine Learning

Location: Remote

Apply link: https://careers-apac-atlassian.icims.com/jobs/17213/senior-data-scientist---machine-learning/job?iis=LinkedIn&iisn=LinkedIn_Job_Ad&mode=job

Requirements:

5+ years of experience in Data Science or related fields

Expertise in applying a broad variety of ML methods including NLP and LLM to solve business problems using large amounts of data

Proven track record of delivering ML projects end-to-end, including designing, development, deployment and monitoring

Ability to communicate and explain data science concepts to diverse audiences, craft a compelling story

Expertise in programming languages such as Python or Java with and the ability to write performant code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)

Agile development mindset, appreciating the benefit of constant iteration and improvement

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