Societe Generale is hiring!
Position: Analyst/ Business Analyst
Qualifications: Bachelor’s/ Master's Degree
Salary: 6.3 - 12 LPA (Expected)
Experience: Freshers/ Experienced
Location: Chennai; Bangalore, India (Hybrid)

📌Apply Now: https://careers.societegenerale.com/en/job-offers/analyst-240000I8-en?src=JB-14381

https://careers.societegenerale.com/en/job-offers/business-analyst-24000IQT-en?src=JB-14381

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Pine Street Inn is hiring Data Analyst

LOCATION: This is a fully remote position with the expectation to come on site occasionally for meetings and/or other work related events.

Apply Link: https://pinestreetinn.hrmdirect.com/employment/job-opening.php?req=3135520&req_loc=279944&&#job

Pays: $62,400 annual DOE

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No one knows about you and no one cares about you on the internet...

And this is a wonderful thing!

Apply for those jobs you don't feel qualified for!

It doesn't matter because almost nobody cares! You can make mistakes, get rejected for the job, give an interview that's not great, and you'll be okay.

This is the time to try new things and make mistakes and learn from them so you can grow and get better.

Moore is hiring Data Analyst

Location: United States

Base Pay: $70000.00 - $100000.00 / Year

Apply Link: https://secure4.saashr.com/ta/6167768.careers?ShowJob=721693330

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Cotiviti is hiring Data Analyst

Apply Link: https://careers-cotiviti.icims.com/jobs/13081/data-analyst/job?mobile=true&width=360&height=708&bga=true&needsRedirect=false&jan1offset=330&jun1offset=330

Qualifications:
Bachelor’s degree in relevant field such as Health Science, Computer Science, Business, or equivalent experience.
2-4 years’ experience with healthcare data analysis. Strong data manipulation skills using database and spreadsheet applications. Knowledge of database applications, including extraction and querying skills.
2+ years’ experience within the Healthcare or Insurance space in a Data Warehouse operations organization.
Strong knowledge of healthcare enrollment, medical claims, and drug claims data required.
Additional knowledge of non-traditional data types (health & wellness, workforce productivity, and EMR) preferred.
2+ years’ experience with HEDIS software.
Ability to use SQL (TOAD, SQL Tools) and other data tools to analyze diverse data sets.
Excellent written and verbal communication skills, with the ability to multitask and prioritize projects to meet scheduled deadlines.
Ability to analyze raw data and disseminate results, with ability to think logically and process sequentially with a high level of detailed accuracy.
Ability to work well independently or in a team environment.
Proficient in Microsoft Office Suite, including PowerPoint, Word, Excel, Outlook and Project

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Data Analytics isn't rocket science. It's just a different language.

Here's a beginner's guide to the world of data analytics:

1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology

2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)

3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?

4) Data Visualization:
- A picture is worth a thousand words

5) Practice:
- There's no better way to learn than to do it yourself.

Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.

It's never too late to start learning!

Company: Gainwell Technologies!
Position: Data Analyst
Experience: Freshers/ Experienced

https://jobs.gainwelltechnologies.com/job/Bangalore-Data-Analyst-KA-560100/1150924900/

Deutsche Bank is hiring!
Position: Analytics - Data Analyst
Qualifications: Bachelor’s/ Master’s Degree
Salary: 7 - 15 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India

📌Apply Now: https://careers.db.com/professionals/search-roles/?test.html%3Fkid%3D=linkedinjobwrap#/professional/job/55301

10 powerful lessons:

1. Embrace Writing to Clear Your Mind
↳ Writing down your thoughts and ideas can help you clarify and organize your thoughts.
↳ Write out your goals and plans to enhance focus and motivation.

2. Always Aim for the Stars
↳ Set ambitious goals that challenge you to grow and learn.
↳ Surround yourself with people who inspire and push you to be your best.

3. Great Leaders Put Others First
↳ Great leaders focus on their team's success, not just their own.
↳ Leadership is not about personal gain, but about positively impacting others.

4. The Power of Task Segmentation
↳ Breaking large tasks into smaller ones can help you feel less overwhelmed and more focused.
↳ Smaller tasks are easier to complete, which can help you build momentum and stay motivated.


5. Reframing Challenges
↳ Embrace challenges as opportunities to learn and grow.
↳ Reflect on failures to identify areas for improvement.

6. Leadership is About Service, Not Power
↳ Leadership is about empowering others to be their best selves.
↳ Great leaders inspire others to innovate and think creatively.

7. The Power of Pen and Paper
↳ Writing helps you understand your own thoughts better.
↳ Write out your thoughts and feelings to gain perspective and clarity.

8. Master the Power of Active Listening
↳ Focus on what others are saying, not on your reply.
↳ Avoid interrupting or formulating your response while the other person is speaking.

9. Writing Sharpens Your Thoughts
↳ Writing forces you to organize your thoughts.
↳ Seeing ideas on paper helps you spot flaws and improvements.

10. Embrace Discipline for Lasting Success
↳ Discipline is choosing between what you want now and what you want most.
↳ Small, consistent actions lead to big results over time.

10 simple yet transformative lessons to shift your mindset.

Companies Offering Data Science Internships

Below is a list of companies that are offering internships in India:

Unstudio AI – ML Intern
Extuent – GenAI Intern
Sponsogram – ML Intern
EarnIn – Data Science Intern
Digitap.ai – Product Analyst Intern
Sony Research – Computer Vision Intern

Here's Part 2 of the phone interview series for data analysts:

𝐓𝐞𝐥𝐥 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞.

𝐇𝐑: [Your Name], can you elaborate on your educational background and any relevant experience you have?

[Your Name]: Certainly! I graduated from [Your University] with a degree in [Your Degree], where I focused on subjects like statistics, data analysis, and programming. During my time there, I worked on several projects that involved analyzing large datasets, using tools like Excel, SQL, and Python.

One of the significant projects I worked on was [Briefly describe a project], where I [mention your role and contributions]. This project helped me develop strong analytical skills and a keen eye for detail.

In addition to my coursework, I completed an internship at [Internship Company], where I was responsible for [specific tasks or projects]. This experience allowed me to apply my theoretical knowledge in a practical setting, and I gained hands-on experience with data visualization tools such as Tableau and Power BI.

𝐇𝐑: That sounds impressive. Can you tell me more about the project you mentioned?

[Your Name]: Sure! The project was about [describe the project in detail, including the goal, your role, and the outcome]. I worked closely with a team of data analysts to clean and process the data, identify key trends, and present our findings to the stakeholders. This experience taught me the importance of clear communication and collaboration in data analysis.

𝐇𝐑: It's great to hear about your hands-on experience. What specific skills do you think you bring to our team?

[Your Name]: I bring a strong foundation in data analysis, excellent problem-solving skills, and proficiency in tools like Excel, SQL, Python, and Tableau. I'm also a quick learner and am eager to continue developing my skills. My ability to work collaboratively and communicate effectively with both technical and non-technical stakeholders is another strength that I believe will be valuable to your team.

𝐇𝐑: Thank you for sharing, [Your Name]. It's good to know about your background and skills.

[Your Name]: Thank you for giving me the opportunity to share!

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Here’s a detailed breakdown of critical roles and their associated responsibilities:


🔘 Data Engineer: Tailored for Data Enthusiasts

1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.

🔘 Data Scientist: Suited for Analytical Minds

6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.

🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts

9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.

🔘 ML Engineer: Tailored for Deployment Enthusiasts

15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.

These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.

Imagen is hiring Senior Data Analyst

Required Qualifications:
4+ years of overall experience
Bachelor's degree (preferably in a quantitative field such as Mathematics or Engineering but not required)
3+ years Healthcare claims data experience with an understanding of medical coding systems (CPT, ICD-10, DRG, etc.)
Developing visualizations for business intelligence (e.g., Tableau dashboards) 
Proficient in SQL, Python (pandas), and Git

Preferred Qualifications:
Proficient in Tableau
Experience with dbt
Knowledge of risk adjustment models

Apply Link: https://boards.greenhouse.io/imagentechnologies/jobs/7533528002

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One day or Day one. You decide.

Data Science edition.

𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.

Swiss Re is hiring!
Position: Data Analyst
Qualification: Bachelor’s/ Master’s Degree
Salary: 8 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India

📌Apply Now: https://careers.swissre.com/job/Bangalore-Data-Analyst-KA/1050003301/

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Here's Part 3 of the phone interview series for data analysts:

𝐃𝐞𝐬𝐜𝐫𝐢𝐛𝐞 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐚 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐩𝐫𝐨𝐛𝐥𝐞𝐦.

𝐇𝐑: [Your Name], can you describe your process for solving a data analysis problem?

[Your Name]: Certainly! When approaching a data analysis problem, I typically follow a structured process that involves several key steps:

1. Understanding the Problem: The first step is to clearly understand the problem at hand. I make sure to define the objectives and identify the key questions that need to be answered. This often involves communicating with stakeholders to ensure we're aligned on the goals.

2. Data Collection: Once the problem is defined, I gather the necessary data. This could involve extracting data from databases, collecting data from various sources, or working with existing datasets. Ensuring data quality is crucial at this stage.

3. Data Cleaning: Data often comes with inconsistencies, missing values, or errors. I spend time cleaning the data to ensure it's accurate and reliable. This step involves handling missing data, removing duplicates, and correcting errors.

4. Exploratory Data Analysis (EDA): After cleaning the data, I perform exploratory data analysis to uncover initial insights and patterns. This involves visualizing the data, calculating summary statistics, and identifying any outliers or trends.

5. Data Modeling: Depending on the problem, I might apply statistical models or machine learning algorithms to analyze the data. This step involves selecting the appropriate model, training it on the data, and evaluating its performance.

6. Interpretation and Presentation: Once the analysis is complete, I interpret the results and draw meaningful conclusions. I create visualizations and reports to present the findings in a clear and concise manner, making sure to tailor the presentation to the audience.

7. Recommendations and Actionable Insights: Finally, I provide recommendations based on the analysis. The goal is to offer actionable insights that can help the stakeholders make informed decisions.

𝐇𝐑: That's a comprehensive process. Can you give me an example of a project where you applied this process?

[Your Name]: Sure! During my internship at [Internship Company], I worked on a project to analyze customer purchase behavior. We aimed to identify patterns and trends to help the marketing team develop targeted campaigns.

𝐇𝐑: Can you walk me through how you applied each step to that project?

[Your Name]: Absolutely. First, I met with the marketing team to understand their objectives and the specific questions they had. We defined our goals as identifying key customer segments and their purchasing habits.

Next, I collected data from the company's CRM and sales databases. The data was then cleaned to remove duplicates and correct any inconsistencies.

During the exploratory data analysis, I used visualizations to identify initial trends and patterns. For example, I discovered that certain customer segments had distinct purchasing patterns during different seasons.

I then applied clustering algorithms to segment the customers based on their behavior. This helped us identify distinct groups with unique characteristics.

The results were presented to the marketing team using dashboards and visualizations created in Tableau. I highlighted the key findings and provided actionable recommendations for targeted marketing campaigns.

𝐇𝐑: That's an excellent example. It sounds like you have a solid approach to tackling data analysis problems.

[Your Name]: Thank you! I believe a structured process is essential to ensure thorough and accurate analysis.

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Accenture is hiring!
Position: Data Science Analytics Associate
Qualification: Any Graduation
Salary: 6 - 9 (Expected)
Experience: Freshers/ Experienced
Location: India

📌Apply Now: https://www.accenture.com/in-en/careers/jobdetails?src=LINKEDINJP&id=AIOC-S01523713_en

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American Express is hiring Business Analyst

Apply Link: https://aexp.eightfold.ai/careers/job/23917280?

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Below is a list of companies that are offering internships in the United Kingdom:

SLB – Data Science Intern
Tencent – NLP Research Intern
Cohere – Research Intern
Viridien – ML Intern
Tencent – Data Product Intern
Watchfinder – Data Engineer Intern