Human Resources Specialist

Looking for referral in business operation and customer success roles.

#referrals #indianstartups #startups Looking for referral in business operation and customer success roles. Zishan Pangarkar Mumbai, India, +917020655084, zishansp96@gmail.com Date of birth 22nd March 1996 Nationality Indian P R O F I L E Experienced supply chain professional with 8 years in the shipping industry. Proficient in SAP ERP, Salesforce, and supply chain management. Skilled in customer success, sales operations, logistics optimization, warehouse management, and vendor relationships. S K I L L S Revenue Growth Leadership Time Management Communication Skills Supply Chain Operations Stakeholder Management E M P L OY M E N T H I S TO RY Feb 2024 Multi-Carrier Inland Partner, Maersk Line Mumbai Enhance tender management through strategic analysis, coordinate inland requirements, and prepare. Negotiation Packages for key clients. Support compliance reporting, client performance measurement, and key initiatives. Jul 2022 — Jan 2024 SCM Customer Success Partner, Maersk Line Mumbai Streamlined supply chain operations, led process improvements, enhanced vendor relationships, and ensured customer satisfaction. Mar 2020 — Jun 2022 Sales Operations Expert - Supply Chain and Logistics, Maersk Line Mumbai Managed Distribution Center performance, P&L, inventory, quality audits, and led team development. May 2019 — Feb 2020 SCM Process Expert, Maersk Line Mumbai Oversaw logistics, warehousing, and inventory, driving cost-efficient initiatives and compliance. May 2017 — Apr 2019 Logistics Senior Associate, Maersk Line Mumbai Coordinated order management, logistics operations, supplier development, and inventory control. Dec 2014 — Nov 2016 Customer Service Associate, Alfa Soft Solutions Pune Prepared documents, managed finance activities, handled escalations, and boosted sales. E D U C AT I O N Jan 2022 — Present Post Graduate Program - Operation Management, Great Lakes Institute of Management Chennai.

How to estimate the total number of Nazi Tanks?

**Historical Context** During World War II, Allied intelligence faced the challenge of estimating German tank production. This led to the development of statistical methods that significantly outperformed traditional intelligence gathering. Here's how the simulation works: 1. We have a secret number of tanks (500 in this case). 2. We pretend to "capture" 5 tanks and look at their serial numbers. 3. Based on these 5 numbers, we try to guess the total number of tanks. 4. We repeat this process 1000 times to see how good our guessing methods are. Here's the strategy: - The "Simple" method (MLE): We just use the highest number we see. - The "Smart" method (Unbiased): We use a slightly more complicated calculation that tries to account for the tanks we didn't see. Observations: 1. The "Simple" method (blue) tends to guess too low. Its average guess is about 416 tanks, which is less than the real 500. 2. The "Smart" method (orange) does better. Its average guess is about 498 tanks, very close to the real 500! 3. But notice how the orange bars are more spread out. This means the "Smart" method can sometimes be way off, even though it's better on average. 4. The "Simple" method is more consistent (the blue bars are more bunched together), but it's consistently too low. **Estimation Methodology** **1. Basic Maximum Likelihood Estimator** The simplest approach uses the maximum observed serial number (m) as an estimator: N̂ = m While simple, this estimator is biased low, as P(N̂ ≤ N) = 1. **Improved Estimators** **Sample Maximum Plus Average Gap** A more sophisticated estimator adds the average gap between observed serial numbers: N̂ = m + (m - k) / k Where: - m: maximum observed serial number - k: number of observed samples This can be interpreted as the maximum plus the average gap, providing a less biased estimate. **Derivation from Order Statistics** The estimator can be derived from order statistics. For a sample of size k from a uniform discrete distribution on {1, ..., N}: E[m] = N * k / (k + 1) Solving for N yields the unbiased estimator: N̂ = m * (k + 1) / k - 1 **Probability Analysis** The probability of observing a specific set of serial numbers {s₁, ..., sₖ} given N tanks is: P({s₁, ..., sₖ} | N) = k! / (N * (N-1) * ... * (N-k+1)) Maximizing this probability (or its logarithm) with respect to N yields the maximum likelihood estimator.

Is SK Hynix's $75 Billion AI Chip Investment a Game-Changer or a Risky Bet?

- I can't believe SK Hynix is dropping $75 billion on AI chips! 💸 - Are they really expecting a massive return on this? 🤔 - This could either revolutionize the industry or be a colossal failure. 😱 - Imagine the advancements in AI if this pays off! 🚀 - But what if it doesn't? Is this the end for SK Hynix? 😬