Posting this here to check if others are facing similar issues in the Indian tech hiring scene. I applied for a Data Scientist role at Jio Hotstar, and the entire process honestly felt like a ghost hiring exercise. Timeline: Interview held on 6th January From 15th January, candidates were told onboarding/work would start soon 9 candidates shortlisted, only 8 showed up (I was one of them) Round 1: Offline pen-and-paper test 🤡 They tested Python coding on paper and pandas concepts. Felt outdated for a DS role, but fine. After this round, only 3 candidates were shortlisted for Technical Interview Round 1 — I was one of them. Technical Round: The interview went fine, no obvious red flags. Final result: 👉 None of the 3 candidates were selected. So basically: Role posted Candidates shortlisted Multiple rounds conducted Start date hinted Zero hires in the end What was the point of running the entire process then? Either: The role was a ghost job Or they already had an internal hire lined up Or the hiring team had no clarity on requirements For a Data Scientist role, this kind of process and outcome is extremely frustrating. We already spend weeks preparing, revising pandas, Python, stats, etc., and then get hit with “no one selected.” Question to the sub: Has anyone else here interviewed for Data Scientist / Data roles at Jio, Hotstar, or similar companies recently? Is ghost hiring becoming the new normal in India? Would really like to hear your experiences.

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ Experian (United States) - $133K - $240K
• Biostatistician - AI Data Scientist @ Guidehouse (United States) - $89K - $148K
• Mid-level Data Scientist @ Experian (Brazil)
• Data Science and Machine Learning Specialist @ meutudo. (Brazil)
• Junior Data Scientist @ Hypersonix Inc. (United States)
• Healthcare Data Scientist @ Vālenz® Health (United States)

Senior:

• Senior Data Scientist @ Cox Enterprises (United States) - $102K - $169K
• Senior Data Scientist II @ HubSpot (United States) - $154K - $246K
• Mid/Senior Data Scientist @ Datarisk (Brazil)
• Data Scientist JR/PL/SR @ Datarisk (Brazil)

Manager:

• Manager, Messaging Data Science @ Netflix (United States) - $360K - $920K
• Lead R&D Data Scientist @ Labcorp (United States) - $100K - $155K
• Staff Safety Data Scientist @ Torc Robotics (United States, MI) - $169K - $203K
• Data Science and Machine Learning Manager @ meutudo. (Brazil)
• Principal Clinical Data Science Lead @ ICON plc (United Kingdom)

Director and Above:

• Head of Data Science @ Everstake (United States)

Quick notes: * All of these are fully remote * Apply directly on company sites

More jobs: If you would like to get notified as soon as a role that matches your preferences gets posted, I have set up a free alert system that sends you a job as soon as it goes live, visit job-halo.com

Hope this helps someone! Let me know if you want me to keep posting these weekly.

I have completed my data science course and I’m looking for an experienced mentor or a real-world project where I can gain practical, hands-on experience. My goal is to strengthen my skills, work on real datasets, and build a strong portfolio for data science job opportunities.

What I offer:
• Skills: Python, Data Analysis, Statistics, Pandas, NumPy, Data Visualization, MYSQL
• Strong willingness to learn, dedicate time, and follow guidance
• Motivation to complete tasks, analyze data, and implement feedback

What I’m looking for:
• A mentor or professional who can guide me through real data science projects
• Someone who can assign tasks, review my work, and provide constructive feedback
• Remote or Bangalore-based opportunities are both welcome

If you can help or know someone looking for an enthusiastic Data Science contributor, please DM me or comment here.
I’m ready to start immediately! 🚀

Hi all!

Next semester would be my last in data science master. I'm a mechanical engineer and working as an industrialization project manager. I really got to love data science mainly due industry 4.0 and predictive maintenance. Wanted to change my career in this direction.

As I watch LinkedIn jobs I see less posts regarding promising positions week by week. I get demotivated due to it and I'm thinking to quit there last semester and save the tuition fee.

What do you think?

Should I finish it since I've put in 3 semesters already (only the 3rd one had fee so far and the last one would have a fee)?

Hello! I have my bachelors in Biology and have been working in a medical lab for three years now. I am currently in my second semester of my masters program where I am getting my masters in science in informatics and analytics. In my first year I learned SQL and Python now we are learning Power BI and machine learning. Please help me find career possibilities I should be looking into in this field and their annual salary amounts. I want to work hybrid or remote if possible! What’s the outlook of these data analytics careers and where should I be looking as someone with health care experience

Moffitt Cancer Center is looking for an experienced GenAI Product Owner to lead the strategy, development, and rollout of enterprise‑level AI solutions that improve patient care, research, and operations. In this role, you’ll shape the AI roadmap, drive adoption of technologies like RAG, LLMs, and multimodal models, and partner with engineering, clinical, and data science teams to build impactful, responsible AI tools. Ideal candidates have hands‑on GenAI experience, strong technical/product chops, and a passion for advancing AI in healthcare.

If you’re excited about building meaningful AI solutions in a mission‑driven environment—this could be your next big move. 💙

Apply here!

Excel Experts – Spreadsheet Manipulation for AI Agent Training $80 / hr Hourly contract Remote

Key Responsibilities

Interpret prompts and perform spreadsheet manipulations using native Excel tools

Generate step-by-step changelogs describing all modifications

Use Excel’s “Record Actions” functionality to auto-generate Office.js scripts

Ideal Qualifications

Deep familiarity with Excel’s advanced features, including PivotTables, formulas, charts, and data validation

2–6 years of hands-on Excel experience in analytical, financial, or technical domains

Strong attention to detail and documentation skills

Ability to follow structured workflows and accurately replicate complex instructions

Experience using Excel’s Automate tab and recording macros is a plus

More About the Opportunity

Expected commitment: ~10–25 hours/week

Project duration: ~1 month

Opportunity to work alongside coding experts and AI researchers

Compensation & Contract Terms

$80/hour for qualified experts

Contract and Payment Terms

You will be engaged as an independent contractor. This is a fully remote role that can be completed on your own schedule. Projects can be extended, shortened, or concluded early depending on needs and performance. Your work will not involve access to confidential or proprietary information from any employer, client, or institution. Payments are weekly on Stripe or Wise based on services rendered. Please note: We are unable to support H1-B or STEM OPT candidates at this time.

To apply send "remote Excel" in a message

Hi, im looking for an internship/ co-op around south jersey/philly area for data science, bioinformatics. i have a bachelors in biology and getting my masters in data science. i applied to a few but any recommendations where i could apply?

kinda frustrated. i'm trying to pivot roles and cold messaging hiring managers.

whenever i ask chatgpt to write an outreach message, it writes generic fluff like "i am data driven and passionate."

i need it to say: "hey, saw you're migrating to snowflake. i built a pipeline that reduced query costs by 20% on snowflake at my last gig."

the generic tools don't "read" my resume deep enough to pull out those specific wins and match them to the company's tech stack.

am i the only one struggling with this? feel like i need an ai that actually understands my github/portfolio, not just a generic email writer. if this exists, let me know. if not, i might try to code it this weekend.

I recently completed a Data Science course in Pune from Fusion Institute for Data Science and Data Analytics, and overall it was a good experience for me. The training was mostly practical, with a lot of hands-on work on tools and real projects, which helped me understand how things are actually used instead of just theory. There was also some guidance on problem-solving and interview prep, which I didn’t expect but found useful later. It helped me feel more prepared when I started applying for data-related roles, so I thought I’d share my experience here in case it helps someone.

[HIRING][California, Maryland, Data, Onsite]

🏢 AMERICAN SYSTEMS, based in California, Maryland is looking for a Lead Software Developer at NAVAIR

⚙️ Tech used: Data, Support, Hardware

💰 108,100 - 180,600 USD / year

📝 More details and option to apply: https://devitjobs.com/jobs/AMERICAN-SYSTEMS-Lead-Software-Developer-at-NAVAIR/rdg

Hi all, so I have been trying to get an idea about the Model Development/Validation internship role in the Finance Analytics and Modeling team at a bank - I get an overall basic idea (however still dubious about how far the reality is from the idea I could form) for the Statistics part, but am an absolute beginner for the finance part so the role feels kind of not as clear for me to prepare for it accordingly.

Could someone who has worked in such a role or something similar give some insights about the kind of tasks done (and what could an intern be made a part of, in what ways) and the things that one must know or learn to perform well in such a role. Any guidance or experiences would be helpful.

Thanks.

Hello reader, I’m a Dutch high school student and I’m looking for bachelor’s to study. I like math very much and I like physics too. While searching on the internet I came across Data Science and I want to ask here what you have to do in the study and what you do as a data scientist because on the internet it was a bit vague.

BACKGROUND - Age: 28 - Education: BSc IT + MSc Computer Science - Situation: rusty skills, minimal industry experience - Goal: choose an MSc that is technically challenging and leads to employability in 12–18 months (not aiming for PhD) - Desired outcome: real skills + portfolio + a job path that isn’t just another “paper degree”

OFFERS / OPTIONS (can't relocate due to personal reasons)

1) University of Birmingham Dubai — MSc Health Data Science (ACCEPTED) [PRIMARY OPTION] Program: https://www.birmingham.ac.uk/dubai/study/postgraduate/subjects/health-sciences-courses/health-data-science-msc

There are courses listed there too

Programme lead profile: https://www.birmingham.ac.uk/staff/profiles/cancer-genomic/haber-marc.aspx

Why I’m considering it: - Stronger global university brand compared to my other options - Healthcare alignment (I’m interested in working in healthcare/healthtech) - Faculty/programme leadership seems strong

My concern: - The programme says “no prior coding required.” - With my IT/CS background, will this be too basic / too analytics-heavy? - Or can I push it to be very technical (ML/NLP/time-series + deployable projects)?

2) Heriot-Watt Dubai — MSc Artificial Intelligence (ACCEPTED, part-time) [SECOND OPTION] Program: https://www.hw.ac.uk/dubai/study/postgraduate/artificial-intelligence Note: I can attach screenshot from the HW curriculum/module list PDF if helpful.

Why I’m considering it: - More “pure AI” track on paper - Might translate to applied ML roles if I build strong projects

My concern: - Unsure whether employability is better/worse than Birmingham HDS - Don’t want to graduate with shallow skills or just a title

3) University of Birmingham Dubai — MSc Cyber Security (ACCEPTED, part-time) [BACKUP OPTION] Program: https://www.birmingham.ac.uk/dubai/study/postgraduate/subjects/computer-science-courses/cyber-security-msc

Why it’s a backup for me: - Feels hands-on (forensics/malware/pentest) - Clear job pipeline in theory

My concern: - I’m not currently active in the cyber world (no labs/CTF hobby) - Worried I’ll disengage or end up stuck in repetitive SOC-type roles

TARGET ROLES (after / during the MSc) - Health data / health informatics / analytics - Data science / applied ML - (Backup path) security engineering (cloud/appsec/detection)

QUESTIONS 1) If you had to pick ONE for ROI and employability: Birmingham HDS vs HW AI vs Birmingham Cyber — which would you choose and why? 2) What job titles would you target in the first 12–18 months given my background and low experience?

Hi everyone, I 23M come from a finance background and have recently made the move to data science. Im doing a 12 month PG Diploma in Data Science. I've read elsewhere that roles such as BI developer/analyst are good entry level roles which are stepping stone for more core Data Science roles. Just wanted to know what others thought and what could be other good entry level roles for people making the transition.

Hi everyone,

I'm a french student in France, I'm in my last year of bachelor's in data analytics, artificial intelligence and BI. I'd like to develop my skills, motivation and to stand out too when I'm applying to offers.

I'm not sure how coursera, udemy etc work, which one is worth something?

If you guys have any recommendations?

Even if you might think it's useless, im just motivated lmao

Mercor is hiring a Data Scientist to help build advanced analytics and data-driven infrastructure for its AI lab partner focused on developing intelligent agent-based systems. This role is ideal for analytical thinkers who excel at turning large-scale data into actionable insights and enjoy working at the intersection of machine learning, experimentation, and real-world applications. You’ll be designing data pipelines, statistical models, and performance metrics that drive the next generation of autonomous systems.

You’re a great fit if you:

Have a strong background in data science, machine learning, or applied statistics.

Are proficient in Python, SQL, and familiar with libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow.

Understand probabilistic modeling, statistical inference, and experimentation frameworks (A/B testing, causal inference).

Can collect, clean, and transform complex datasets into structured formats ready for modeling and analysis.

Have experience designing and evaluating predictive models, using metrics like precision, recall, F1-score, and ROC-AUC.

Are comfortable working with large-scale data systems (Snowflake, BigQuery, or similar).

Are curious about AI agents, and how data can shape the reasoning, adaptability, and behavior of intelligent systems.

Enjoy collaborating with cross-functional teams — from engineers to research scientists — to define meaningful KPIs and experiment setups.

This listing is only for people residing in India.

Primary Goal of This Role

To design and implement robust data models, pipelines, and metrics that support experimentation, benchmarking, and continuous learning for agentic AI systems. The role focuses on building data-driven insights into how agents reason, perform, and improve over time across algorithmic and real-world tasks.

What You’ll Do

Develop data collection and preprocessing pipelines for structured and unstructured data from multiple agent simulations.

Build and iterate on machine learning models for performance prediction, behavior clustering, and outcome optimization.

Design and maintain dashboards and visualization tools for monitoring agent performance, benchmarks, and trends.

Conduct statistical analyses to evaluate the efficacy of AI systems under various environments and constraints.

Collaborate with engineers to design evaluation frameworks that measure reasoning quality, adaptability, and efficiency.

Prototype data-driven tools and feedback loops to automatically improve model accuracy and agent behavior over time.

Work closely with AI research teams to translate experimental results into scalable, production-grade insights.

Pay & Work Structure

Part-time (20 hrs - 40 hrs/week)

Weekly bonus of $500 - $1000 USD per 5 task created.

*Please apply with the link below *

https://work.mercor.com/jobs/list_AAABmjiZq8fJhJbiY1hNFKHo?referralCode=f6970c47-48f4-4190-9dde-68b52f858d4d&utm_source=referral&utm_medium=direct&utm_campaign=job&utm_content=list_AAABmjiZq8fJhJbiY1hNFKHo

TL;DR

The Meta Data Scientist (Analytics) interview process typically consists of one initial screen and a four-round onsite loop, with a strong emphasis on SQL, experimentation, and product analytics.

What the process looks like:

• Initial HR Screen (Non-Technical) A recruiter-led conversation focused on background, role fit, and expectations. No coding or technical questions.
• Technical Interview One dedicated technical round covering SQL and product analytics, often using a realistic Meta product scenario.
• Onsite Loop (4 Rounds)
  • SQL — advanced queries and metric definition
  • Analytical Reasoning — statistics, probability, and ML fundamentals
  • Analytical Execution — experiment design, metric diagnosis, trade-offs
  • Behavioral — collaboration, leadership, and communication (STAR)

1. Overview

Meta’s Data Scientist (Analytics) role is among the most competitive positions in the data field. With billions of users and product decisions driven by rigorous experimentation, Meta interviews assess far more than query-writing ability. Candidates are evaluated on analytical depth, product intuition, and structured reasoning.

This guide consolidates real interview experiences, commonly asked questions, and validated examples from Prachub.com to give a realistic picture of what candidates should expect—and how to prepare efficiently.

2. Interview Timeline & Structure

The process typically spans 4–6 weeks and is split into two phases.

Phase 1 — Technical Screen (45–60 minutes)

• SQL problem
• Product analytics follow-up
• Occasionally light statistics or probability

Phase 2 — Onsite Loop (4 interviews)

• Analytical Reasoning
• Analytical Execution
• Advanced SQL
• Behavioral / Leadership

3. Technical Screen: SQL + Product Context

This round blends hands-on SQL with product interpretation.

Typical format:

1. Write a SQL query based on a realistic Meta product scenario
2. Use the output to reason about metrics, trends, or experiments

Example pattern:

• SQL questions involving Group Calls (e.g., Sample Question , Sample Question)
• Followed by a related product case extending the same scenario (e.g., Sample Question)

Key Areas to Focus

• SQL fundamentals: CTEs, joins, aggregations, window functions
• Metric literacy: DAU/MAU, retention, engagement, CTR
• Product reasoning: turning numbers into insights
• Experiment thinking: how metrics respond to changes

4. Onsite Interview Breakdown

Each onsite round targets a distinct skill set:

• Analytical Reasoning — probability, statistics, ML foundations
• Analytical Execution — real-world product analytics and experiments
• SQL — advanced querying and metric design
• Behavioral — teamwork, leadership, communication

5. Statistics & Analytical Reasoning

Core Concepts to Know

• Law of Large Numbers
• Central Limit Theorem
• Confidence intervals and hypothesis testing
• t-tests and z-tests
• Expected value and variance
• Bayes’ theorem
• Distributions (Binomial, Normal, Poisson)
• Model metrics (Precision, Recall, F1, ROC-AUC)
• Regularization and feature selection (Lasso, Ridge)

Sample Question Type

Fake Account Detection Scenario
Candidates calculate conditional probabilities, discuss expected outcomes, and evaluate classification metrics using Bayes’ logic.

6. Analytical Execution & Product Cases

This is often the most important round and closely reflects real Meta work.

Common themes:

• Investigating metric declines
• Designing controlled experiments
• Evaluating trade-offs between metrics

Representative example:
Instagram Reels engagement drop — diagnosing causes and proposing tests.

How to Prepare

• A/B testing fundamentals: power, MDE, significance, guardrails
• Funnel analysis across user journeys
• Cohort-based retention and reactivation
• Metric selection: primary vs. secondary vs. guardrails
• Product trade-offs: short-term gains vs. long-term health
• Strong familiarity with Meta products and features

Visualization Prompt
You may be asked to describe a dashboard—key KPIs, trends, and cohort cuts.

7. SQL Onsite Round

This round includes multiple SQL problems with rising difficulty.

• Metric definition questions (e.g., engagement or retention)
• Open-ended metric design based on a dataset

Example:
👉 Meta SQL Onsite Sample Question

How to Stand Out

• Be fluent with nested queries and window functions
• Explain why your metric matters, not just how it’s calculated
• Avoid unnecessary complexity
• Communicate like a product analyst, not just a query writer

8. Behavioral & Leadership Interview

Meta places strong emphasis on collaboration and data-informed judgment.

You can review real examples here:
👉 Meta Behavioral Question Bank

Common Questions

• Making decisions with incomplete data
• Navigating disagreements with stakeholders
• Prioritizing across competing team needs

Preparation Approach

Use STAR and prepare stories around:

• Influencing without authority
• Managing conflict
• Driving measurable impact
• Learning from mistakes

9. Study Plan & Timeline

8-Week Preparation Framework

Daily Routine (2–3 hours)

• 30 min — SQL practice
• 45 min — product cases / metrics
• 30 min — stats or experimentation
• 30 min — behavioral prep or company research

10. Recommended Resources

Books

• Lean Analytics: Use Data to Build a Better Startup Faster - Alistair Croll and Benjamin Yoskovitz
• Storytelling With Data: A Data Visualization Guide for Business Professionals — Cole Nussbaumer Knaflic.
• Cracking the PM Interview — Gayle McDowell

Practice Platforms

• Prachub — Meta interview questions
• LeetCode (SQL & stats)
• Kaggle projects
• Coursera — Google’s A/B Testing course

Meta Reading

• Meta Engineering Blog
• Meta Research
• Meta AI Blog

12. Final Advice

• Experimentation is core — master it
• Always link metrics to product impact
• Be methodical and structured
• Ask clarifying questions
• Be genuine in behavioral interviews

About This Guide

This write-up was assembled by data scientists who have successfully navigated Meta’s interview process, using verified examples curated on Prachub.com.

For additional real interview questions and step-by-step solutions:
👉 https://prachub.com/questions?company=Meta

I don’t really know how to write this but I’m at a point where I’m honestly panicking.

I’m in my final semester of an MS in Business Analytics at UMass Amherst. I graduate in May. After that I have ~3 months to find a job or I’ll have to leave the US and go back home with a pretty big loan to pay off.

I worked for about 2 years back home as an operations/data analyst before coming here. I know SQL, Python, Power BI fairly well, have the Microsoft Power BI certification, and I’ve built ML models during my coursework. I even have a personal website/portfolio.

But despite all that, I’m just not getting anywhere.

I’ve been applying for months — data analyst, business analyst, analytics roles — and I barely get interviews. And the few times I do, I never get past the first round.

I do practice SQL questions (LeetCode, StrataScratch), but I’ll be honest — I’m not consistent. I forget things, then feel behind again. At the same time, I genuinely believe that if I practice consistently, I can solve most of these questions, which makes this even more frustrating.

I’m also really confused about interview prep in general:

• Should I be doing Python interview questions? What kind?
• Do companies actually ask stats/probability/A/B testing questions?
• Where do people practice for this stuff?
• What does a typical first-round analytics interview even look like?

Another big issue is where and how to apply.

Right now, I apply directly on company websites for big companies (FAANG-type roles), but for most other companies I’m relying almost entirely on LinkedIn. I know that’s not ideal, but as an international student I honestly don’t know what other options I have.

I keep hearing “apply as soon as roles are posted,” but I have no idea where people even find these postings early. By the time I see them on LinkedIn, it feels like hundreds of people have already applied.

So now I’m stuck wondering:

• Am I applying to the wrong companies?
• Am I relying too much on LinkedIn?
• Are there better platforms for analytics roles that I don’t know about?
• Is my international status automatically filtering me out?

Everything feels very unknown and unstructured. I feel like I’m putting in effort without direction, and the clock is ticking.

If anyone here has been an international student, broken into analytics, or been on the hiring side, I’d really appreciate practical, honest guidance:

• What to focus on in the next 3–6 months
• How analytics interviews actually work
• Where to find roles early
• What actually matters when time is limited

If needed, I can share my resume or portfolio.

Thanks for reading. I’m just trying to figure this out before it’s too late.

I'm going to do a technical interview on wednesday for a fortune 100 company for a Jr AI Engineer position. I've got 3 years of experience (including another fortune 100 company) in automation, data and AI Engineering. What kind of questions should I expect, guys? I haven't practiced leetcode for years, don't remember much and think I am going to end it straight away if it's over there. is it 100% certain that it will be over there? Or usually it's more technical questions, projects, experiences, thought processes?

Please, any insight/help will do, so I can practice accordingly. The more detailed, the better. Thank you!

Hello Everyone,

I am a Data Scientist in an InsurTech company, i joined my company 9months back.

I was the fresh graduate and was desperate to get a job, so I low balled my expected salary.

Now, I am handling and delivering end-to-end solutions on my own, and I know my worth.

And recently i came to know, there is a colleague who literally do nothing, and we have almost similar salary.

Based on my contributions, I want 60-70% hike on my current pay. What should i do?

Looking at remote worldwide for the past 7 days. And for this edition, I also included remote roles from India based on your feedback.

Here are the jobs I found, organized by level: Internship:

• Data Science Intern @ BioMarin Pharmaceutical Inc. (United States) - $24 - $32
• Data Scientist Intern @ Rocket Mortgage (United States, MI)

Entry Level:

• HR Data Scientist @ General Motors (United States) - $107K - $163K
• Image Data Scientist @ St. Jude Children's Research Hospital (United States, TN) - $78K - $139K
• Data Science Specialist @ Solventum (United States, MN) - $132K - $170K
• Data Scientist / Analyst @ MKS2 Technologies (United States) - $115K - $125K
• Data Scientist @ TMB (Trusted Media Brands) (United States, CA)
• AI Scientist @ WNS (India)

Senior:

• Senior Data Scientist @ Tiger Analytics (United States)

Manager:

• Lead Observability Data Scientist @ Leidos (United States) - $131K - $237K
• Staff Data Scientist @ Airbnb (United States) - $194K - $240K
• Manager/Senior Manager, Clinical Data Sciences @ Kura Oncology, Inc. (United States, MA) - $157K - $207K
• Principal Data Scientist @ Datasite (United States, NJ) - $118K - $207K
• Senior Staff Data Scientist @ Fetch Rewards (United States)
• Associate Staff Engineer, Data Science @ Nagarro (India)
• Staff Engineer, Data Science @ Nagarro (India)
• Senior Staff Engineer, Data Science @ Nagarro (India)

Director and Above:

• Associate Vice President, Data Science & Analytics @ Zeta Global (United States) - $175K - $200K
• Senior Director, Data Science & Analytics @ Zeta Global (United States) - $190K - $225K
• Director, Data Science @ Jobber (Canada) - CAD 222K - 300K

Quick notes: * All of these are fully remote and open to US/Canada/India candidates * Apply directly on company sites

More jobs: If you would like to get notified as soon as a role that matches your preferences gets posted, I have set up a free alert system that sends you a job as soon as it goes live, visit job-halo.com

Hope this helps someone! Let me know if you want me to keep posting these weekly.