Paypal hiring Data Scientist 1
Apply link: https://paypal.eightfold.ai/careers/job?domain=paypal.com&pid=274907105521&query=R0126369&domain=paypal.com&sort_by=relevance&job_index=0
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All the best ๐๐
Apply link: https://paypal.eightfold.ai/careers/job?domain=paypal.com&pid=274907105521&query=R0126369&domain=paypal.com&sort_by=relevance&job_index=0
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
๐1
Optum hiring Data Scientist
Apply link: https://careers.unitedhealthgroup.com/job/22011510/data-scientist-hyderabad-in/?src=JB-22455&utm_source=linkedin.com&utm_medium=job_posting
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Apply link: https://careers.unitedhealthgroup.com/job/22011510/data-scientist-hyderabad-in/?src=JB-22455&utm_source=linkedin.com&utm_medium=job_posting
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
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All the best ๐๐
๐1
Capgemini hiring Data Scientist
Apply link: https://careers.capgemini.com/job/Kolkata-Data-Scientist-C/1208045701/?feedId=388933&utm_source=LinkedInJobPostings
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All the best ๐๐
Apply link: https://careers.capgemini.com/job/Kolkata-Data-Scientist-C/1208045701/?feedId=388933&utm_source=LinkedInJobPostings
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๐2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐๐ฎ๐๐.๐ฎ๐ถ ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ โ ๐ง๐ต๐ฒ ๐๐ฒ๐๐ ๐๐ฟ๐ฒ๐ฒ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ผ ๐ฆ๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ ๐๐ผ๐๐ฟ๐ป๐ฒ๐๐
Look no further! The Fast.ai Deep Learning Course is one of the most beginner-friendly and practical resources available โ and the best part? Itโs completely FREE๐๐
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โค1
Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
โค1
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ง๐ผ๐ฝ ๐ ๐ก๐๐ ๐ข๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
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Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified ๐
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
Infosys :- https://pdlink.in/4jsHZXf
IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified ๐
Apple hiring Machine Learning Engineer
Apply link: https://jobs.apple.com/en-us/details/200604647/machine-learning-engineering-manager-photos
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
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All the best ๐๐
Apply link: https://jobs.apple.com/en-us/details/200604647/machine-learning-engineering-manager-photos
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๐๐ฅ๐๐ ๐ง๐๐ง๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ๐
Gain Real-World Data Analytics Experience with TATA โ 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!
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Gain Real-World Data Analytics Experience with TATA โ 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!
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Stylumia is hiring Machine Learning Engineering Intern
For 2025 grads
Location: Bangalore
https://apply.workable.com/stylumia/j/6D2519412E/
For 2025 grads
Location: Bangalore
https://apply.workable.com/stylumia/j/6D2519412E/
Workable
Stylumia Intelligence Technology
Stylumia is dedicated to augmenting human intelligence in retail for a better world. With relevant data and the right technology, people and businesses in fashion and lifestyle retail can solve challenging problems and improve the world!!
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
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Enroll For FREE & Get Certified๐๏ธ
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified๐๏ธ
Forwarded from Jobs | Internships | Placement | Interviews
Google hiring Data Scientist
Apply link: https://careers.google.com/jobs/results/79726425535324870-data-scientist/
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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All the best ๐๐
Apply link: https://careers.google.com/jobs/results/79726425535324870-data-scientist/
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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All the best ๐๐
Statistics Interview Questions
Topics to Cover:
โข Descriptive statistics
โข Probability
โข Hypothesis testing
โข Regression analysis
Questions and Answers:
1 Q: What is the difference between descriptive and inferential statistics?
A: Descriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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Topics to Cover:
โข Descriptive statistics
โข Probability
โข Hypothesis testing
โข Regression analysis
Questions and Answers:
1 Q: What is the difference between descriptive and inferential statistics?
A: Descriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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โค5
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ด ๐๐ฒ๐๐ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ, ๐ ๐๐ง & ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ๐
๐ Learn Data Science for Free from the Worldโs Best Universities๐
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online โ and theyโre 100% free. ๐ฏ๐
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All The Best ๐
๐ Learn Data Science for Free from the Worldโs Best Universities๐
Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online โ and theyโre 100% free. ๐ฏ๐
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Data Analyst vs Data Scientist: Must-Know Differences
Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing descriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
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Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing descriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
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โค3
Forwarded from Python for Data Analysts
๐ฐ ๐ฃ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐๐ฟ๐ฒ๐ฒ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ฎ๐๐ฎ๐ฆ๐ฐ๐ฟ๐ถ๐ฝ๐, ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐/๐ ๐ & ๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ ๐
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners๐
Learning tech doesnโt have to be overwhelmingโespecially when you have a roadmap to guide you!๐๐
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners๐
Learning tech doesnโt have to be overwhelmingโespecially when you have a roadmap to guide you!๐๐
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Enjoy Learning โ ๏ธ
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, youโre sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
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โค5
Forwarded from Python for Data Analysts
๐ฑ ๐๐ฅ๐๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to build job-ready tech skills from top companies โ all for free?๐จโ๐
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jnOv16
All The Best ๐
Want to build job-ready tech skills from top companies โ all for free?๐จโ๐
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jnOv16
All The Best ๐
Reality check on Data Analytics jobs:
โถ Most recruiters & employers are open to different backgrounds
โถ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
โถ Excel - every job needs it
โถ SQL - data retrieval and manipulation
โถ Data Visualization - Tableau, Power BI, or Excel (Advanced)
โถ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
โถ Communication
โถ Teamwork & Collaboration
โถ Problem Solver
โถ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
โถ Most recruiters & employers are open to different backgrounds
โถ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
โถ Excel - every job needs it
โถ SQL - data retrieval and manipulation
โถ Data Visualization - Tableau, Power BI, or Excel (Advanced)
โถ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
โถ Communication
โถ Teamwork & Collaboration
โถ Problem Solver
โถ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
โค2
Forwarded from Jobs | Internships | Placement | Interviews
Atlassian hiring Senior Data Scientist
Apply link: https://globalcareers-atlassian.icims.com/jobs/19662/senior-data-scientist/job
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Apply link: https://globalcareers-atlassian.icims.com/jobs/19662/senior-data-scientist/job
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
โค3