Customer Service Specialist
Teleperformance is seeking a proactive customer service specialist to join their international support team. The role focuses on handling inbound calls, resolving client queries, and maintaining high service standards. Successful candidates will manage CRM documentation and collaborate with internal teams to resolve complex issues. The position operates on a hybrid model with rotational shifts.
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Experience
Experience not specified
Function
Support
Work mode
Hybrid, India
Company
Tier 2
What you will work on
Teleperformance is seeking a proactive customer service specialist to join their international support team. The role focuses on handling inbound calls, resolving client queries, and maintaining high service standards. Successful candidates will manage CRM documentation and collaborate with internal teams to resolve complex issues. The position operates on a hybrid model with rotational shifts.
TAL's take
A standard support role for a large BPO with clear, if generic, performance expectations and hybrid flexibility.
The JD is clear and coherent regarding the scope of work for a customer support professional, despite the lack of specific technical stack requirements.
Salaries at Teleperformance
6.0 LPA average
Based on 45 Grapevine salary entries for Teleperformance.
Other
0 - 2 years | L2
5 LPA average
Range: 5 - 5 LPA
Operations
0 - 2 years | L2
4 LPA average
Range: 4 - 4 LPA
Design
0 - 2 years | L2
2 LPA average
Range: 2 - 2 LPA
Finance
0 - 2 years | L2
3 LPA average
Range: 3 - 3 LPA
Must haves
- Manage inbound calls from international customers
- Provide accurate information and resolve queries
- Document interactions in CRM systems
- Ensure compliance with company policies and data security
- Meet performance targets for quality and resolution time
About the company
Teleperformance is a large, established global BPO services company, fitting the mid-tier classification.
Posts mentioning Teleperformance
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Teleperformance Feedback
This Research Paper changed my life forever.
It was one of the papers that was discussed in my interview at Goldman. I came to know about this research paper a few years back after consulting a friend doing an ML PhD at University of Maryland, College Park. The explanation of the paper: 1. Initialize the neural network with small random values typically (-0.1,0.1) to avoid symmetry issues. 2. Now get ready to do Forward propagation: you pass thetraining data through the multilayer perceptron and compute the output. For each neuron in the MLP, calculate the weighted sum of its inputs and apply the activation function. (my favourite is tanh for LSTM applications) 3. Now compute the loss using a loss function like mean squared error, between output computed and the actual value. 4. Now get ready to do backpropagation, where you need to calculate the gradient of the loss function with respect to each weight by propagating the error backward through the network. 5. So, compute partial derivatives of the loss with respect to each weight, starting from the output layer and moving back to the input layer. 6. Here is the fun part: update the weights using the gradients obtained from the backward pass. here people usually use adam optimizer, which allows for accelerated stochastic gradient descent. Fun trivia: Adam stands for "Adaptive Moment Estimation". 7. Now repeat the forward and backward propagation process for numerous tries until theperformance of the model stabilizes.