Machine Learning Engineer - Deep Learning

San Francisco, California
US$180000 - US$200000 per year + Competitive Benefits

Machine Learning Engineer - Deep Learning
San Francisco Bay Area
$180,000 - $220,000 + Competitive Benefits

The Company

Harnham is working with a late-stage venture that is paving the way for machine learning. This organization is looking to disrupt how supply chain analytics are performed and I can almost guarantee you know who they are. If you want to take your expertise to the big (data) table and join this company's advanced technology group - Let's talk.

You will be at the center of the action - working closely with executive leaders and management across different functions of the organization! With your remarkable experience implementing a technical and business acumen, you will have an opportunity to make major decisions in the infrastructure and direction of the team.

This organization will be working on revolutionary algorithms and deep learning is their bread and butter. You will be improving the way people interact online and in-stores. This organization is globally recognized and you will be part of that magic! They are not letting good talent fly by - and are investing all efforts to give the best experience to consumers and employees, alike.

Did I mention you get to work with machine learning legends?

The Role

You will:

  • Build, implement, and optimize machine learning and deep learning models for a better consumer experience
  • Communicate with stakeholders and leaders from various verticals to define business goals and deliver business insight with deployable machine learning algorithms and influence decisions
  • Demonstrate a high level of ownership of tasks at hand, with the liberty of taking an approach and making it yours
  • Collaborate with world-class data scientists and engineers to make an impact in the world!

Skills and Expertise

You have:

  • Expertise in Python to productionize machine learning developments
  • HUGE PLUS! Industry experience deploying models based on Deep Learning, Reinforcement Learning, and/or Bayesian statistics frameworks
  • Strong capacity to work with Big Data using Hive, Hadoop, Cassandra, Kafka, and/or Spark, as well as hands-on experience working with GCP or AWS
  • Extensive knowledge of Machine Learning and Deep Learning model production for content analysis and ops research problems
  • Exceptional communication skills and a passion for data!

Benefits

$180,000- $220,000 + Competitive Benefits

  • Bonus and Equity Options
  • Great 401K package
  • Green Card Sponsorship
  • Shuttle System and Commuter Benefits
  • Generous Maternity/Paternity Leave

*Open to Visa Sponsorship/Transfer!*

How to Apply

Please register your interest using the Apply button on this page.

For more information this role or other Data Science and Machine Learning roles, please contact Karla Guerra at Harnham.


KEYWORDS
Python, R, Machine Learning, Natural Language Processing, NLP, Recommendation Engine, Recommender System, Spark, AWS, GCP, Amazon, Google, SQL, Hadoop, Hive, Bigquery, HDFS, Flink, Beam, Kafka, MySQL, NoSQL, Cassandra, HBase, Bigtable, Shell, Perl, Bash, Ruby, Java, Scala, Travis CI, Jenkins, Deep Learning, Bayesian Statistics, Text Analytics, Thompson Sampling, Probability, Multi-Class, Decision Tree, Vector Machine, Reinforcement Learning, Reward System, Modelling, Algorithm, Content Analysis, Sentiment Analysis, Data Scientist, Data Science, Machine Learning, Machine Learning Engineer, Big Data, Scikit, Scikit-learn, TensorFlow, PyTorch, Keras, NumPy, CNN, RNN, NLTK, LSTM

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KMG-01
San Francisco, California
US$180000 - US$200000 per year + Competitive Benefits
  1. Permanent
  2. Deep Learning and AI

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Data Science Interview Questions: What The Experts Say

Our friends at Data Science Dojo have compiled a list of 101 actual Data Science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the Data Science industry – Amazon, Microsoft, Facebook, Google, Netflix, Expedia, etc.  Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it reflects in the interview. They've divided the questions into six categories:  Machine LearningData AnalysisStatistics, Probability, and MathematicsProgrammingSQLExperiential/Behavioural Questions Once you've gone through all the questions, you should have a good understanding of how well you're prepared for your next Data Science interview. Machine Learning As one will expect, Data Science interviews focus heavily on questions that help the company test your concepts, applications, and experience on machine learning. Each question included in this category has been recently asked in one or more actual Data Science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview.  Explain Logistic Regression and its assumptions.Explain Linear Regression and its assumptions.How do you split your data between training and validation?Describe Binary Classification.Explain the working of decision trees.What are different metrics to classify a dataset?What's the role of a cost function?What's the difference between convex and non-convex cost function?Why is it important to know bias-variance trade off while modeling?Why is regularisation used in machine learning models? What are the differences between L1 and L2 regularisation?What's the problem of exploding gradients in machine learning?Is it necessary to use activation functions in neural networks?In what aspects is a box plot different from a histogram?What is cross validation? Why is it used?Can you explain the concept of false positive and false negative?Explain how SVM works.While working at Facebook, you're asked to implement some new features. What type of experiment would you run to implement these features?What techniques can be used to evaluate a Machine Learning model?Why is overfitting a problem in machine learning models? What steps can you take to avoid it?Describe a way to detect anomalies in a given dataset.What are the Naive Bayes fundamentals?What is AUC - ROC Curve?What is K-means?How does the Gradient Boosting algorithm work?Explain advantages and drawbacks of Support Vector Machines (SVM).What is the difference between bagging and boosting?Before building any model, why do we need the feature selection/engineering step?How to deal with unbalanced binary classification?What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?Why is dimensionality reduction important?What are hyperparameters, how to tune them, how to test and know if they worked for the particular problem?How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession, and gender? Define a machine learning algorithm for this.How will you inspect missing data and when are they important for your analysis?How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?What are time series forecasting techniques?How does a logistic regression model know what the coefficients are?Explain Principle Component Analysis (PCA) and it's assumptions.Formulate Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) techniques.What are neural networks used for?40. Why is gradient checking important?Is random weight assignment better than assigning same weights to the units in the hidden layer?How to find the F1 score after a model is trained?How many topic modeling techniques do you know of? Explain them briefly.How does a neural network with one layer and one input and output compare to a logistic regression?Why Rectified Linear Unit/ReLU is a good activation function?When using the Gaussian mixture model, how do you know it's applicable?If a Product Manager says that they want to double the number of ads in Facebook's Newsfeed, how would you figure out if this is a good idea or not?What do you know about LSTM?Explain the difference between generative and discriminative algorithms.Can you explain what MapReduce is and how it works? If the model isn't perfect, how would you like to select the threshold so that the model outputs 1 or 0 for label?Are boosting algorithms better than decision trees? If yes, why?What do you think are the important factors in the algorithm Uber uses to assign rides to drivers?How does speech synthesis works? Data Analysis Machine Learning concepts are not the only area in which you'll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We're grouping all such questions under this category. Data Analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual Data Science interviews at the companies listed above.   What are the core steps of the data analysis process?How do you detect if a new observation is an outlier?Facebook wants to analyse why the "likes per user and minutes spent on a platform are increasing, but total number of users are decreasing". How can they do that?If you have a chance to add something to Facebook then how would you measure its success?If you are working at Facebook and you want to detect bogus/fake accounts. How will you go about that?What are anomaly detection methods?How do you solve for multicollinearity?How to optimise marketing spend between various marketing channels?What metrics would you use to track whether Uber's strategy of using paid advertising to acquire customers works?What are the core steps for data preprocessing before applying machine learning algorithms?How do you inspect missing data?How does caching work and how do you use it in Data Science? Statistics, Probability and Mathematics As we've already mentioned, Data Science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for Data Science, and these topics are always brought up in data science interviews. Here is a list of statistics and probability questions that have been asked in actual Data Science interviews. How would you select a representative sample of search queries from 5 million queries?Discuss how to randomly select a sample from a product user population.What is the importance of Markov Chains in Data Science?How do you prove that males are on average taller than females by knowing just gender or height.What is the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP)?What does P-Value mean?Define Central Limit Theorem (CLT) and it's application?There are six marbles in a bag, one is white. You reach in the bag 100 times. After drawing a marble, it is placed back in the bag. What is the probability of drawing the white marble at least once?Explain Euclidean distance.Define variance.How will you cut a circular cake into eight equal pieces?What is the law of large numbers?How do you weigh nine marbles three times on a balance scale to select the heaviest one?You call three random friends who live in Seattle and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of lying. All three say "yes". What's the probability it's actually raining? Explain a probability distribution that is not normal and how to apply that?You have two dice. What is the probability of getting at least one four? Also find out the probability of getting at least one four if you have n dice.Draw the curve log(x+10) Programming When you appear for a data science interview your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions. You should expect a few programming/coding questions in your data science interviews. You interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes you typically write in a given week.  Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their Data Science interviews.  Write a function to check whether a particular word is a palindrome or not.Write a program to generate Fibonacci sequence.Explain about string parsing in R languageWrite a sorting algorithm for a numerical dataset in Python.Coding test: moving average Input 10, 20, 30, 10, ... Output: 10, 15, 20, 17.5, ...Write a Python code to return the count of words in a stringHow do you find percentile? Write the code for itWhat is the difference between - (i) Stack and Queue and (ii) Linked list and Array? Structured Query Language (SQL) Real-world data is stored in databases and it ‘travels’ via queries. If there's one language a Data Science professional must know, it's SQL - or “Structured Query Language”. SQL is widely used across all job roles in Data Science and is often a ‘deal-breaker’. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their Data Science interviews.  How would you handle NULLs when querying a data set?How will you explain JOIN function in SQL in the simplest possible way?Select all customers who purchased at least two items on two separate days from Amazon.What is the difference between DDL, DML, and DCL?96. Why is Database Normalisation Important?What is the difference between clustered and non-clustered index? Situational/Behavioural Questions Capabilities don’t necessarily guarantee performance. It's for this reason employers ask you situational or behavioural questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioural question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you're a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared!  Here are some situational/behavioural questions that large tech companies typically ask:    What was the most challenging project you have worked on so far? Can you explain your learning outcomes?According to your judgement, does Data Science differ from Machine Learning?If you're faced with Selection Bias, how will you avoid it?How would you describe Data Science to a Business Executive? If you're looking for new Data Science role, you can find our latest opportunities here.  This article was written by Tooba Mukhtar and Rahim Rasool for Data Science Jojo. It has been republished with permission. You can view the original article, which includes answers to the above questions here. 

The Harnham 2019 Data & Analytics Salary Guide Is Here

We are thrilled to announce the launch of our 2019 UK, US and European Salary Guides. With over 3,000 respondents globally, this year’s guides are our largest and most insightful yet.  Looking at your responses, it is overwhelmingly clear that the Data & Analytics industry is continuing to thrive. This has led to an incredibly active market with 77% of respondents in the UK and Europe, and 72% in the US, willing to leave their role for the right opportunity.  Salary expectations remain high, although we’re seeing that candidates often expect 2-10% more than they actually achieve when moving between roles.  Globally, we’ve also seen a change in the reasons people give for leaving a position, with a lack of career progression overtaking an uncompetitive salary as the main reason for seeking a change.   There also remains plenty of room for industry improvement when looking at gender parity; the UK market is only 25% female and this falls to 23% in the US and 21% across the rest of Europe.  In addition to our findings, the guides also include insights into a variety of markets and recommendations for both those hiring, and those seeking a new role.  You can download your copies of the UK, US and European guides here.

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