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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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How to accelerate Data Science job search in 2021

Chayan
Chayan Last Updated : 04 Dec, 2020
8 min read

This article was published as a part of the Data Science Blogathon.

Introduction

A job search can be the most frustrating periods of your life. It is worse if you’re a fresher, or if you’re trying to break into a new field such as Data & Analytics.

An average time period for a job search can take more than 5 months of endless applications with very few actual interviews.

However, this process can be done and achieved within 1-3 months, if actioned upon properly. Let me break down the 6 step strategy.

The 6 step strategy that I suggest to follow:

  1. Gaining the right skills & competencies
  2. A great resume
  3. Having a targeted resume with keywords
  4. Planning a targeted job search
  5. Utilizing LinkedIn for job opportunities
  6. Leveraging community & connections

Let’s dive right in!

1. Right Skills & Competencies

Needless to say, acquiring the right skills is of utmost essential to get any job you want. You need to make a list and roadmap of the minimum skills required to get the data science job you want and then start working towards it.

But how to know what skills are required?

Well, the quickest and the smartest way would be to just go to the job portals and search for the role you want. Scan every job description and see what skills are required by most of the organizations for the same role. Another way would just be to explore and search on the internet – be it Quora, medium blogs, or LinkedIn.

Building the skillset

Once you know what roles you want to target and what skills to acquire, you’d need to make a study plan and the amount of time required. If you’re currently working, then you’d need to spare at least 1 hour daily during the weekdays and a minimum of 10 hours during the weekend, which is 15 hours/week on average.

Depending on the skills you need, you’d need to either opt for some courses or other online/offline ways to skill up and do the required projects to showcase your potential.

For example, if you are targetting for a Data Scientist job role, you’d need to learn the basic skills first and then apply them by doing projects, participating in Kaggle competitions, Analytics Vidhya Hackathons, etc. Showcasing these actual work will increase your chances of getting calls by a huge magnitude. Just make sure to not do the basic Machine Learning projects like Titanic, House Price prediction, etc. Your resume should have 3 good and unique projects about which you can talk in detail.

2. Resume

So now you have the required skills and projects in your portfolio. The next step will be to make an awesome resume which will fetch your calls and make recruiters interested in you. Mind you, an awesome resume doesn’t mean using fancy colors and templates.

Dos & Don’ts of a resume

I have made my set of rules and checkpoints that I recommend while making a resume for a job in the tech field. These are my views that I have gotten with experience looking at 100+ resumes.

  • First and foremost, your resume should be on 1 page, unless you’re 15+ years of experience and have mammoth amounts of experience and achievements.

  • Your resume should be crisp, concise, and include all the relevant information only. Adding all your history of work, projects, and all the skills you have learned from your birth wouldn’t make your resume great. Only include relevant information.

  • Choose a good template that clearly shows all the work experience and skills. Note, the simpler the better.

  • Avoid including your photo.

  • Do not include fancy icons and rating systems to showcase your skill level. All such things make it harder for the ATS(Applicant Tracking System) to scan and parse the information.

  • Use action verbs while describing the work experience and projects.

  • Make sure points are short and include your task and results achieved.

  • Put the sections that are relevant(such as current work experience, personal projects) on top, and sections less relevant to the bottom(such as your education). A bad resume example:

 

data science job bad resume

The resume in the above picture uses too many fancy colors and icons which are just not required. It not only makes it look bad but also makes the ATS difficult to parse the text in it.

A good resume:

data science job - good resume

Source

The above resume template checks all the boxes and can be modified according to your needs. The simpler the resume is, the better it will be. There is often a misconception to make your resume stand-out, you need to make it fancy and eye-catching. That is false.

 

3. Targeted Resume and Keywords

Your resume should not include all the information and skills you have and let the recruiter find what (s)he requires from it. It should only contain what the recruiter wants. Your resume should portray a picture that you are the candidate that is made for that job.

What does a targeted resume mean?

A targeted resume means modifying your resume to better suit the job you’re applying for. For example, if you’re applying for a Data Scientist job that focuses on NLP skills, then try to showcase and highlight the NLP tasks you did in your current job. Alternatively, showcase more NLP based personal projects and include all the NLP skills as well. In essence, reframe your resume for the job to make it a targeted resume and not a generic resume where the recruiter has to find what he/she wants.

Secondly, add the keywords mentioned in the Job Description. Job Descriptions include many skills and keywords which they expect from the candidate. You might have most of them, but you might have used different words in your resume or skipped them.

For example, a job description mentions: “Data cleaning, Linear & Non-Linear modeling, Sklearn, Pandas” And your resume has: “Data Preprocessing, regression, classification, Python, scikit-learn”. Now there’s the need to remove the skills you have with the skills/keywords mentioned in the JD. This is to maximize your chances to score high on the ATS which scores the resumes based on the number of keywords found in your resume and accordingly put it in the list. An HR might only pick the top few to manually check and then shortlist. Resumes with very low ATS scores might be automatically rejected by the ATS itself. This is the reason why a lot of applications get an automated rejection shortly after applying.

Having said all this, it is not necessarily important to include every keyword in the JD and change your resume for every job, but modify it slightly if you feel there needs to be some change.

4. Targeted job search

A normal and inefficient way to apply for jobs would be to mindlessly submit your resume to every job posting you find on job portals and hope to get callbacks. While you might get some, but mostly you will get a very poor response. This can be due to multiple reasons: the company is not active on those platforms, or your resume is not good enough. But the main reason is the number of applications they receive. On average, a Data Science job posting gets 200 applications within 24 hours, where the vacancies are not more than 5 or 10

You see the issue there? There is a lot of competition and your application needs to stand out and be visible to the hiring manager. The first step is to make a targeted resume and the second is to reach out to the hiring manager or the recruiter. The aim is to get their email-id and directly mail them your application/resume. This way you get direct access to their mailbox, whereas all other applicants’ resumes would be still in their database.

For example,

“Hi XYZ, I came across this job posting on ABC and I think I might be the right candidate for the role. I have N years of experience and my skills include JKL. It would be great if you could take a look at my resume and see if I fit. Thanks”

Getting referrals

The first step in the targeted job search by referrals is to make a list of potential companies you’d want to work with and have those roles you want. The next step is to connect with the people in those companies via LinkedIn. The employees working there can help you with referrals which is the best way to get a job. The strategy is to connect with at least 5 people in a company and start the conversation with them about the company/work. After having some conversation, ask them if they’d be willing to refer you to the role at their company if they have any vacancies.

Now, this strategy might not work 100% as you’d expect because the people might either not accept your connection request, or might not respond to your message. Or even if they do, they might not have any vacancies at the company. Therefore, the best way to maximize your chances is to add more people which will increase the probability of getting referrals. Also, do some sort of research about the job postings and be ready with the Job ID you want to be referred for. Just asking “Please help with a referral” will get you nothing. Have a professional template ready where you just change the names and the Job ID and send it to the concerned person.

For example,

First message:

“Hi, ABC. I see you’re working at XYZ as a Data Scientist. I was keen to know about your company and your role.”

Second message: “Great! I have been looking for similar roles for some time now and was wondering if you’d be willing to put in a referral for me. The job ID is 12345. Thanks!”

5. Utilizing LinkedIn

LinkedIn is the best way to expand your network and ultimately job opportunities. Although the LinkedIn jobs portal gets a lot of job postings, I did not have a good experience with it. A better way to search for data science job roles on LinkedIn is to search for people who are hiring. Just go to the search window and search for words like “Hiring Data Scientist” (or the role you want). Connect with them and showcase your profile and resume.

Another way is to remain active on the platform and stay on look for people posting for data science vacancies and job updates. There are a lot of vacancies which get filled by this way and the ones who respond first get the advantage. The trick to getting more such opportunities is to connect with more and more people and be always on the look.

Have conversations with other people in your domain and tell them about your job hunt. Ask them to send you any vacancies they come across. This way you leverage their connections and their active time on LinkedIn.

Lastly, the most important strategy is to publish content on LinkedIn. Writing posts regularly will not only increase your connections and followers but also attract recruiters to your profile. Hence comes the need for an up to date LinkedIn profile. Make sure you have a good Profile photo, summary, About, projects, certifications, etc. You can post about coding problems you solved on Leetcode/Hackerrank, post about your Kaggle projects, etc.

6. Community & Connections

Community and the close connections you make are other great sources of opportunities. A lot of vacancies get filled up this way. Connect to people in your domain and have conversations with them. The more you converse, the better the relationship becomes. Ask for their contact numbers and stay in touch with them. This way they’ll forward you any job posting they come across in Whatsapp/Telegram/Slack groups.

Speaking of chat groups, many communities have a group on these channels where there are a lot of opportunities being posted each day. The active people who are quick to act on those are the ones who get the advantage. Most of these data science job roles are never even posted on job portals or LinkedIn. So make sure to be a part of such communities and maximize your chances of landing a great job.

For example, some of the great communities/groups are ML with Harshit Alhuwalia, Co-Learning Lounge-AI Room, KaggleNoobs, etc.

 

Wrapping Up

Job search in Data science is a tough task and needs to be planned separately. Consider it as the task of the highest priority and spend 30 minutes daily on the points we discussed above. Having a time period to get a job should be in your mind as well. Following this strategy and optimizing your data science job hunt would surely bring results. Remember to be patient and not get burnt out by the frustration. It happens to the best of the people and you’ll only grow from it. Just keep this growth mindset in yourself and work towards the goal until you achieve it!

Connect to me on LinkedIn if you have any queries or want some more insights.

Chayan
Chayan

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