Top Companies Hiring Data Scientists in June 2024: Data science is one of the top technologies that are making huge strides in global industries. A career in data science is lucrative and rewarding. The availability of massive amounts of data and demand for data science skills has skyrocketed. Be it a tech or a non-tech company, all industries are deploying data to ensure they deliver customer-centric products. Here we have listed the top companies hiring data scientists in June 2024.
Infosys - Bengaluru East, Karnataka, India
Requirements:
Master's degree in Computer Science, Statistics, Mathematics, or a related field.
Over seven years of experience in data science and machine learning, primarily focusing on model development and deployment.
Expert-level knowledge of statistics, probability theory, hypothesis testing, and statistical inference.
In-depth knowledge about machine learning algorithms like linear regression, logistic regression, decision trees, random forests, and ensemble learning.
Proficiency in Python programming and hands-on experience using data science libraries such as pandas, scikit-learn, numpy, Pytorch/Keras, and TensorFlow.
Experience using cloud computing platforms, preferably Google Cloud Platform (GCP).
Good hands-on experience in data visualization using tools like matplotlib, seaborn, or Tableau.
Good communication and presentation skills with experience in preparing technical reports.
Gather and process large datasets to identify key trends, patterns, and insights from the data.
Develop and implement machine learning models using Python libraries like pandas, scikit-learn, numpy, Pytorch/Keras, and TensorFlow.
Measure and improve model performance and generalizability.
Share data insights in a clear and concise manner with business stakeholders using data visualization techniques and storytelling.
Work with data engineers, software developers, and business stakeholders in translating the data science work into real-world products and services.
Keep up to date with the latest trends and technologies in data science, machine learning, and artificial intelligence.
Experience in NLP and CV techniques.
Familiarity with DevOps methodologies and practices for CI/CD.
Experience with data warehousing/data lakes solutions—BigQuery/Snowflake.
Real-time data processing and streaming analytics.
Desire to learn and follow data science and machine learning progress.
Apply Here
Swiggy - India
Key Responsibilities:
1. Work closely with the logistics team to optimize Assignment and Batching processes.
2. Develop and deploy machine learning models to improve logistics efficiency and accuracy.
3. Conduct data analysis and modeling to identify opportunities for optimization and automation.
4. Collaborate with cross-functional teams, including software developers and product managers, to integrate data-driven solutions into our systems.
5. Take ownership of projects from inception to delivery, ensuring high-quality and impactful results.
6. Stay updated on the latest advancements in machine learning and logistics optimization techniques.
1. Bachelor's degree or higher in Applied Mathematics, Statistics, Computer Science, or a related field.
2. 3 to 5 years of hands-on experience in industry or research lab settings.
3. Strong expertise in machine learning algorithms and statistical methods.
4. Proven track record of developing and shipping ML data products.
5. Proficiency in Python.
6. Experience with Spark is a plus.
7. Excellent communication and collaboration skills, with the ability to work effectively in a team environment.
8. Previous experience in deep learning and working in startup or product-based consumer/internet companies is preferred.
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Mastercard - Gurgaon, Haryana, India
Key Responsibilities:
Work closely with global optimization solutions team to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support data insights and analytical needs across products, markets, and services
The candidate for this position will focus on Building solutions using Machine Learning and creating actionable insights to support product optimization and sales enablement.
Prototype new algorithms, experiment, evaluate and deliver actionable insights.
Drive the evolution of products with an impact focused on data science and engineering.
Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
Perform data ingestion, aggregation, and processing on high volume and high dimensionality data to drive and enable data unification and produce relevant insights.
Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.
Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions.
A superior academic record at a leading university in Computer Science, Data Science, Technology, mathematics, statistics, or a related field or equivalent work experience
2-5 years of experience in data management, data mining, data analytics, data reporting, data product development and quantitative analysis
Strong analytical skills with track record of translating data into compelling insights
Prior experience working in a product development role.
Knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
Proficiency in using Python/Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), and SQL to build Big Data products & platforms
Experience with Enterprise Business Intelligence Platform/Data platform i.e. Tableau, PowerBI is a plus.
Demonstrated success interacting with stakeholders to understand technical needs and ensuring analyses and solutions meet their needs effectively.
Ability to build a strong narrative on the business value of products and actively participate in sales enablement efforts.
Able to work in a fast-paced, deadline-driven environment as part of a team and as an individual contributor.
Apply Here
Adani Group - Greater Kolkata Area
Responsibilities
Developing and implementing advanced algorithms and models for image recognition, segmentation, and feature extraction.
Collaborating with cross-functional teams to design and optimize image processing pipelines and workflows.
Contributing to the development and enhancement of AI-based systems for image interpretation and understanding.
Utilizing machine learning and deep learning techniques to extract meaningful insights from image data.
Evaluating and validating the performance of image processing algorithms through rigorous testing and analysis.
Communicating complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.
Participating in the creation and maintenance of high-quality documentation and technical reports related to image science projects.
Continuously exploring new methodologies and technologies to improve the efficiency and accuracy of image analysis processes.
Adhering to industry best practices and standards while ensuring compliance with data privacy and security regulations.
2-3 years of experience in the Data Science and AI industry with a focus on image processing and analysis.
Bachelor’ or master’s degree or Ph.D in Computer Science, Data Science, Artificial Intelligence, or a related field.
Certifications in machine learning, computer vision, or image processing would be an advantage.
Strong analytical and critical thinking skills.
Excellent problem-solving abilities and attention to detail.
Effective communication and collaboration skills.
Adaptability and willingness to learn and explore new technologies.
To stay updated on job openings with leading companies in data science, you can:
Follow the careers pages and social media profiles of these companies.
Set up job alerts on job search websites with relevant keywords.
Join professional networking platforms like LinkedIn and follow companies and recruiters in the data science field.
Engage with data science communities and forums where job opportunities are often shared.
Attend industry events, conferences, and meetups where companies may announce job openings or recruit talent.
The responsibilities of a data scientist in a top company often include:
Collecting, cleaning, and analyzing large datasets to extract actionable insights.
Developing machine learning models and algorithms to solve business problems.
Collaborating with cross-functional teams to understand business needs and translate them into analytical solutions.
Communicating findings and recommendations to stakeholders through data visualization and storytelling.
Continuously researching and staying updated on advancements in data science and technology to drive innovation within the company.
Top companies typically look for data science candidates with:
Strong programming skills in languages like Python, R, or SQL.
Proficiency in data manipulation and analysis using libraries like pandas, NumPy, and scikit-learn.
Knowledge of statistical methods and machine learning techniques.
Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.
Excellent communication and teamwork skills to collaborate effectively with colleagues from diverse backgrounds.
A solid understanding of business concepts and the ability to translate business problems into data-driven solutions.
Advanced degrees (Master's or Ph.D.) in fields like computer science, statistics, mathematics, or related disciplines may also be preferred by some companies.
To stand out when applying to top data science positions, you can:
Highlight your relevant skills, experiences, and achievements in data analysis, machine learning, and programming.
Quantify your accomplishments using metrics or numbers whenever possible.
Customize your resume and cover letter for each job application to emphasize how your background aligns with the specific requirements of the position.
Showcase any projects, publications, or certifications that demonstrate your expertise in data science.
Use keywords and phrases from the job description to optimize your resume for applicant tracking systems (ATS) used by many companies.
Proofread your documents carefully to ensure they are free of errors and present a professional image.
Common interview questions and challenges for candidates applying to top data science roles may include:
Technical questions assessing your knowledge of algorithms, statistics, and machine learning concepts.
Problem-solving questions that require you to apply your analytical skills to real-world scenarios.
Behavioral questions to evaluate your communication, teamwork, and problem-solving abilities.
Case studies or take-home assignments where you're asked to analyze data and present your findings.
Challenges related to working with messy or incomplete datasets and deriving meaningful insights.
Demonstrating your ability to explain complex technical concepts to non-technical stakeholders.
Keeping up with rapidly evolving technologies and methodologies in the field of data science.