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Hire the top 1% of Data Scientists

Souraj M

Souraj M

Data Scientist

10+ years experience  •  Full-time availability

Verified Skills

check Core Java - 10 yrs | Expert
check Data Science - 10yrs | Advanced

Other Skills

Java SQL Jenkins SOAP Java 8 +3
Yash D

Yash D

Data Scientist

9+ years experience  •  Full-time availability

Verified Skills

check Data Science - 9 yrs | Expert
check Ruby- 7 yrs | Advanced

Other Skills

Python PostgreSQL Redis CSS HTML +3
Ashutosh D

Ashutosh D

Data Scientist

6+ years experience  •  Full-time availability

Verified Skills

check Java- 6 yrs | Expert
check Data Science - 5 yrs | Expert

Other Skills

SQL Rust React Machine Learning MySQL +3
Mansi U

Mansi U

Data Scientist

4+ years experience  •  Full-time availability

Verified Skills

check Spring - 4 yrs | Expert
check Data Science - 4 yrs | Advanced

Other Skills

Mockito Jenkins Data engineering Python +3
Prajwal G

Prajwal G

Data Scientist

5+ years experience  •  Full-time availability

Verified Skills

check Data Science - 5 yrs | Expert
check SQL - 4 yrs | Advanced

Other Skills

Ruby Spark HDFS HTML CSS +3
Aakash D

Aakash D

Data Scientist

7+ years experience  •  Full-time availability

Verified Skills

check Java - 5 yrs | Expert
check Data Science - 7 yrs | Expert

Other Skills

SQL Rust Machine Learning HDFS MySQL +3

Hire Data Scientists For Work Like

From food to FinTech, thousands of companies use Supersourcing to hire, scale and grow faster.

Predictive Modelling

Predictive Modelling

Data Scientists use historical data to build models that can predict future outcomes, such as sales, customer behaviour, or equipment failure.

Customer Analytics

Customer Analytics

Data Scientists analyze customer data to gain insights into their behavior, preferences, and demographics, which can inform marketing and sales strategies.

Fraud Detection

Fraud Detection

Data Scientists use machine learning algorithms to detect patterns of fraudulent behavior in financial transactions, insurance claims, and other data.

Natural Language Processing

Natural Language Processing (NLP)

Data Scientists use NLP techniques to process and analyze unstructured text data such as emails, social media posts, and customer reviews, to extract insights and sentiment.

Computer Vision

Computer Vision

Data Scientists use Computer Vision techniques to process and analyze visual data such as images and videos, to identify patterns and extract insights.

Supply Chain Optimization

Supply Chain Optimization

Data Scientists use optimization techniques and models to analyze data from various sources such as inventory, sales and logistics data to optimize the supply chain operations and improve efficiency.

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The entire process takes around 2-10 days. A clear job description and fast interview turnarounds can reduce this duration.

Supersourcing takes the responsibility of managing employees timesheet, availability. One Senior Account manager will be assigned to each project. We don't prefer bot on support. Our senior team is available even in weekends to support you in your business. Just an Email/WhatsApp away.

Firstly, we understand their technical knowledge through Mettl & HackerEarth. Secondly, we manually verify all data points through different sources to ensure the highest quality of talent.

We don't work with freelancers. We work with developers who are looking for full-time work but at different organisations. The verification interview is also done to ensure seamless compatibility with different companies.

Monthly to yearly, we have different options that companies can choose from.

We assign every company an account manager. Please do reach out to your point of contact to add and remove developers as per requirement.

Yes you can hire them on permanent basis, after 6 months of contact pay one fixed finding fees and hire them on your payroll, Try before you buy. We are really flexible depends on your need.

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Other Platforms Vs Supersourcing

Sourcing

Duration 10 Days

Posting your requirement in multiple job Boards

You have to create an account in multiple job boards and paying for its subscription

Sourcing process illustration Sourcing process illustration
Duration 1 Day

Just share your requirements with us

Role • Experience • Communication • Technical Skills • Etc.

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Duration 20 Days

Spending your enormous time in screening unskilled candidates

You will be wasting most of your time and money in the screening process

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Duration 2 Days

Our AI will do the screening and match the top 1 profile for you

Programming

Communication

Situation-Based Questions

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Super curated profiles don't require interviews. 100% matching with your requirement. Supersourcing guarantees quality.

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Duration 10 Days

Onboarding

After completing all the steps, you will send an offer letter to the candidate, unsure of whether they will accept it or not.

Onboarding process illustration Onboarding process illustration
Duration 2 Days

Our team will take care of everything

Onboarding Support

Dedicated Account Manager

Standard Timesheet

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Onboarding process illustration Onboarding process illustration

Traditional method of Hiring is slow, costly with no guarantee of joining

Time Spent

30 Days

Hiring Cost

$30,000+

Supersourcing is smart, technology driven and designed for future

Less than a week

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Data Scientist

What is a Data scientist?

A data scientist is a professional who uses statistical and programming skills to extract insights from large and complex data sets. They use a combination of tools and techniques from fields such as statistics, machine learning, and computer science to analyze and interpret data. The goal of a data scientist is to identify patterns and trends in data that can inform business decisions or drive innovation.

Hire Data Scientists to work on projects such as building predictive models, creating data visualizations, and designing experiments. They use programming languages such as Python and R to work with data and perform statistical analysis, as well as tools like SQL and Hadoop to manage large data sets. They also use machine learning libraries and frameworks such as scikit-learn, TensorFlow and PyTorch to build models.

Data Scientists often collaborate with other teams such as product managers and engineers to ensure that their insights are translated into actionable outcomes. They also communicate their findings to stakeholders in a clear and concise manner, using data visualization and storytelling techniques.

Data science is a multidisciplinary field that requires a strong background in mathematics, statistics, and computer science, as well as the ability to think critically and creatively. It's a rapidly growing field that plays a critical role in many industries, including finance, healthcare, technology, and retail.

Why Hire Data Scientists?

By hiring Data scientists, an organization can gain a competitive advantage by leveraging data to make more informed decisions and gain a deeper understanding of their customers and operations. Additionally, data scientists can help companies identify new opportunities for growth and revenue, and can help organizations to become more data-driven. There are several reasons why companies hire Data Scientists:

  • To gain insights and make data-driven decisions

    Data Scientists use their skills in statistics, machine learning, and programming to extract insights from large and complex data sets. They can identify patterns and trends in the data that can inform business decisions, such as identifying new market opportunities or improving operational efficiencies.

  • To build predictive models

    Data Scientists can use their skills in machine learning to build models that can make predictions about future events, such as customer behavior or market trends. These models can be used to inform strategic decision-making, such as product development or marketing campaigns.

  • To improve customer experience

    Data Scientists can use their skills in data analysis and visualization to identify customer pain points and opportunities for improvement. They can also use machine learning to personalize customer experiences and develop targeted marketing campaigns.

  • To gain a competitive advantage

    Companies that are able to effectively harness the power of data can gain a competitive advantage in their industry. Hiring Data Scientists can help companies to stay ahead of the curve by identifying new trends and opportunities, and by developing innovative solutions.

  • To comply with regulations

    Some industries are heavily regulated, and companies must comply with data privacy and security regulations. Hiring Data Scientists can help companies to comply with these regulations by developing and implementing data management and security protocols.

Why Hire Remote Data Scientists ?

There are several reasons why a company might choose to hire a remote data scientist:

  • Access to a wider pool of talent

    Hiring Data scientists remotely allows a company to access a larger pool of qualified candidates, regardless of their location. This can be especially beneficial for finding specialized skills or niche expertise that may not be available in a specific geographic area.

  • Cost savings

    Hiring remote Data Scientists can save a company money on overhead costs such as office space and equipment, as well as reduced recruitment expenses.

  • Increased productivity

    Remote workers often have more flexibility to manage their own schedules, which can lead to increased productivity and output.

  • Flexibility

    Hiring Data scientists remotely allows for more flexibility in terms of work hours, which can help a company to operate 24/7.

  • Increased diversity

    Hiring Data scientists remotely can also increase diversity within a company, as it can bring in employees from different backgrounds, cultures and experiences.

  • Access to the latest technology

    Remote Data Scientists can have access to the latest technology and tools, which can help them to be more efficient and productive.

  • Better work-life balance

    Remote work can provide employees with a better work-life balance, which can lead to increased job satisfaction and employee retention.

  • Overall, Hiring Data scientists can help companies to make better use of their data, improve their decision-making, and gain a competitive edge in their industry.

Types of Data Scientist

Data scientists are skilled professionals who can help organizations make informed decisions by analyzing and interpreting complex data. They use a combination of statistical analysis, machine learning, and domain expertise to extract insights from data, which can be used to improve business operations, inform strategic decisions, and drive growth. Data science is a broad field with many different specializations. Here are a few types of Data Scientists:

  • Business Intelligence Analyst

    Data Scientists who specialise in using data visualization tools such as Tableau and PowerBI to create dashboards and reports that help business leaders make data-driven decisions.

  • Machine Learning Engineer

    Data Scientists who specialise in building and deploying machine learning models, often using libraries such as scikit-learn and TensorFlow.

  • Natural Language Processing (NLP) Data Scientist

    Data Scientists who specialise in using NLP techniques to process and analyze text data such as social media posts, customer reviews and emails.

  • Computer Vision Data Scientist

    Data Scientists who specialise in using computer vision techniques to process and analyze visual data such as images and videos.

  • Data Engineer

    Data Scientists who specialise in data infrastructure, including data storage, data processing, and data pipeline development.

  • Big Data Scientist

    Specializes in handling and analyzing large and complex data sets using technologies such as Hadoop and Spark.

  • Deep Learning Data Scientist

    Data Scientists who specialise in using deep learning techniques and neural networks to analyze data and build predictive models.

  • Time Series Data Scientist

    Data Scientists who specialise in using statistical and machine learning techniques to analyze time series data and build forecasting models.

  • Recommender System Data Scientist

    Data Scientists who specialise in creating personalized recommendation systems using methods such as collaborative filtering, matrix factorization, and deep learning.

  • Anomaly Detection Data Scientist

    Data Scientists who specialise in identifying unusual patterns or events in data that deviate from the norm, which can indicate fraud, errors, or other issues.

Responsibilities of a Data Scientist

The goal of a data scientist is to extract insights and value from data, and to use that information to drive business decisions and improve organizational performance. The responsibilities of a data scientist can vary depending on the specific role and industry, but generally include:

  • Collecting, cleaning, and organizing large data sets.
  • Analyzing and interpreting complex data using statistical and machine learning techniques.
  • Creating and implementing models to predict future trends and behaviors.
  • Communicating findings and insights to stakeholders through data visualizations and reports.
  • Collaborating with cross-functional teams to inform and drive business decisions.
  • Continuously monitoring and analyzing performance of models and making recommendations for improvements.
  • Staying current with new data technologies and techniques to ensure the organization remains competitive.

Tools used by Data Scientists

Data Scientists use a variety of tools to collect, process, analyze, and visualize data. Some common tools include:

  • Programming languages such as Python and R for data manipulation, analysis, and visualization.
  • Data storage and management tools such as SQL, NoSQL, and Hadoop for storing and retrieving large data sets.
  • Machine learning libraries and frameworks such as scikit-learn, TensorFlow, and Keras for building and training models.
  • Visualization tools such as Matplotlib, ggplot, and Tableau for creating interactive and informative data visualizations.
  • Collaboration and project management tools such as Jupyter Notebook, GitHub and Git for sharing and version controlling code and data.
  • Cloud platforms such as AWS, Azure, and Google Cloud for data storage, computation, and deployment of models.
  • Business Intelligence tools such as Power BI, Looker, and QlikView, which are very helpful in creating interactive and informative dashboards.

FAQs on Hiring Data Scientists

Here are some frequently asked questions about hiring a data scientist:

1. What qualifications should a data scientist have?

A data scientist should have a strong background in mathematics, statistics, and computer science, as well as experience with programming languages such as Python and R, and machine learning libraries and frameworks. A master's degree or PhD in a related field such as statistics, computer science, or electrical engineering is often preferred, but many Data Scientists also have a bachelor's degree in a technical field and significant industry experience.

2. What specific skills should a data scientist have?

A data scientist should have expertise in statistical analysis, machine learning, data visualization, and programming. They should also be familiar with big data tools such as Hadoop and Spark, as well as databases and SQL. They should have good communication skills, and ability to work with cross-functional teams.

3. How do you evaluate a data scientist's qualifications?

To evaluate a data scientist's qualifications, you can ask them to complete a coding test or data analysis project, as well as conduct technical interviews that focus on their experience with specific tools and techniques. You can also ask for references and check their portfolio of past projects or publications.

4. What should a data scientist be able to do?

A data scientist should be able to collect, clean, and organize large data sets, analyze and interpret complex data using statistical and machine learning techniques, create and implement models to predict future trends and behaviors, communicate findings and insights to stakeholders through data visualizations and reports, and continuously monitor and analyze performance of models and make recommendations for improvements.

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