Developpeur Python
Paris, Île-de-France / €65000 - €75000
INFO
€65000 - €75000
LOCATION
Paris, Île-de-France
Permanent
DÉVELOPPEUR PYTHON- TÉLÉ TRAVAIL HYBRIDE
PARIS (75)
65-75K EUR
Cette agence data reherche son Développeur Python Senior pour reprendre le lead sur le développement d'application pour un mono client. Si vous avez déjà travaillé sur des problématiques web sur Python, ce poste est fait pour vous!
VOTRE RÔLE
Rattaché(e) au manager technique, vous serez en charge de :
- la conception, le développement, le test et la mise en production
- participer a la définition de l'architecture et a sa mise en place
- d'être réfèrent et coach de profils plus juniors
- développer des APIs
VOTRE PROFIL
- Bac +4/5 en Ecole d'ingénieur ou profil similaire
- Vous avez une forte expérience sur un rôle similaire
- Votre indispensable est la maîtrise de Python
- Vous utilisez des framework Python tels que Django ou Flask
- La maîtrise d'un could est un plus (AWS idéalement)
- Bon niveau d'anglais

SIMILAR
JOB RESULTS

Weekly News Digest: 10th – 14th January 2022 | Harnham Recruitment post
+
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.PYMNTS.com: Fighting fraudulent transactions, by the numbersHow are banks using AI and other tools to curb transaction fraud?In 2021, PYMNTS interviewed banking executives to determine how acquiring banks use artificial intelligence (AI) and effective merchant monitoring to combat credit, debit, and prepaid card fraud. In this piece, it shares the results of the interviews:Most acquiring banks say fraudulent transactions increased between 2020 and 202193 percent of those surveyed said they saw a year-to-year increase in fraud. 88 per cent said reducing fraud is critical to their ability to increase or maintain merchant processing revenue.Most banks that use AI use it for fraud detectionAlmost all (98 per cent) of acquirers using AI said it has found fraud detection. 60 per cent have said AI is the best tool for them to detect fraud, while another 15 per cent said it’s an important weapon.Most banks outsource this workFraud detection is too important for some banks to spend years developing their own complex system. So, 92 per cent of banks that use AI systems for fraud prevention and detection said they outsource the systems.To read more about this, click here. Analytics Insight: Top Python machine learning libraries to explore in 2022What Machine Learning libraries should you be focusing on this year? Python is the most popular programming language for data science projects, while machine learning is globally trending. According to Analytics Insight, Python machine learning libraries have become the language for implementing machine learning algorithms. So, to fully understand Data Science and Machine Learning, Python is essential. Here are the top Python machine learning libraries to help you begin your Python journey, and what they’re most useful for:TensorFlow: an open-source numerical computing library for machine learning based on neural networks.PyTorch: used for natural language processing, computer vision, and other similar kinds of tasks.Keras: machine learning toolset that aids companies such as Square, Yelp and Uber.Orage3: includes tools for machine learning, data mining, and data visualisation. Numpy: includes robust computing capabilities within the large, high performance programming communitySciPy: a core tool for accomplishing mathematical, scientific and engineering computations.SciKit-Learn: an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others.Pandas: has powerful data frames and flexible data handling.Matplotlib: replaces the need to use the proprietary MATLAB statistical language. Theano: allows for simultaneous computing, fast execution speed and optimised stability. To read more about this, click here. Analytics India Mag: Why should data engineers learn Scala?Is Scala beneficial to a Data Engineer? Scala combines object-oriented and functional programming in one concise, high-level language, and its static types help avoid bugs in complex applications. Scala does have some key advantages such as its use of data-parallel operations, simple structure suitable for big data processors, and its high-volume capabilities. On the other hand, the article points out why Scala might not be beneficial to a Data Engineer:Difficult to learn Not widely adoptedOnly 10 per cent of jobs require Scala knowledge While Scala does not occupy the same level of importance as other popular languages, it’s certainly a useful language to learn if it matches a data engineer’s career goals. To read more about this, click here. Forbes: Data analytics marathon – why your organisation must focus on the finishIn this Forbes piece, the author compares analytics to a marathon – both take commitment preparedness, and endurance to be successful. A companies’ analytics will go through several cycles as business priorities shift and evolve. They are explained here as milestones of the Data & Analytics marathon:Data collectionData preparationData visualisation Data analysis Insight communicationTake action The author, Brent Dykes, notes that many drop off at the last mile in the race, the action phase where analytics teams perform analysis, share their insights and then implement changes to optimise the business. Most companies have no problem with the start of the data analytics marathon, but many of them aren’t completing the entire race. In order to finish the data analytics race in a strong position, companies and analytics teams must align the data with the business strategy and follow these three steps.Automate early-stage tasksNarrow the scopeFoster a stronger data cultureTo read more about this, click here. We’ve loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

What’s Hot in NYC’s Data Market? Modern Analytics Engineering is on the Rise
+
New York has always set the stage for what’s next. When it comes to the latest in the tech stack, it’s modern Analytics Engineering is the latest addition to the Data and Analytics industry. The role of Analytics Engineer is one of the newer positions in the world of Data, and in NYC, a hub of media, advertising, and e-commerce – it’s emerging as one of the most in-demand markets in New York and beyond.
Why You Might Need an Analytics Engineer
Data-driven businesses interested in building value for their customers often turn to a mix of Analytics and Data Modelling Engineer. The Data Engineer creates the infrastructure, platform development, and Data movement for the purpose of Machine Learning and Analytics downstream. Ultimately, the Analytics Engineer role is quite similar to the typical Data Engineer but differs in that it doesn’t involve platform development or infrastructure the same way.
Analytics Engineering is a relatively new term within the last five years and are coming into this field from a variety of backgrounds. But the most in-demand background moving into this role is Data Engineering. Why? For the most part, it’s those individuals who can not only script in Python but also do Python programming on the backend.
Key Aspects of this Role:
- Warehouse architecture (e.g., Snowflake, Redshift, BigQuery), and Data Modeling with a popular and relatively new tool dbt (originally Fishtown Analytics), for use by Analysts.
- ETL Development
- Data visualization
- Other tech such as Fivetran, Stitch, and Python
With SQL and Data modelling being the real meat and potatoes of the position, people often move into an Analytics Engineering position that requires little Python experience – however, the salary you can expect if Engineering or Data Science experience and proficiency in Python is substantially higher. It poses an interesting opportunity for Analysts, Data modellers, and Data visualization folks interested in learning a modern engineering stack to make a transition into a more technical, and higher-paying role.
Why You May Want to Consider an Analytics Engineering Role
People move into this role from careers as Analysts, Data Scientists, Data Engineers, and even Software Engineers, a unique career progression in this industry. For the already heavily technical professionals – this is a role that provides both engineering challenges and the chance to work close to the business. Wherever you are on your career journey, Analytics Engineer is a great opportunity from a career growth perspective and can help get you where you want to go. You’re no cog in the wheel here. As an Analytics Engineer, you can help drive decisions that make an impact for your company.
Analytics Engineers on Your Team Can Drive Value for Your Business
Though this position is relatively new in the grand scheme of technological advances to help drive business, it is in demand and growing exponentially. So, it’s important to know if you’re business needs someone to fill this role, you need to know what you’re looking for. For companies, whose main objective is making Data-driven decisions regarding customer retention, marketing campaign conversion, supply chain analytics, etc.
The role of the Analytics Engineer can be a perfect addition to both managing large amounts of Data coming into the businesses and helping drive value.
Take a look at our latest Analytics Engineer jobs here or get in touch with one of our expert consultants to find out more:
For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com.
For our Arizona Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com.
For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

Weekly News Digest: August 1st – 5th | Harnham Recruitment post
+
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.
TOWARDS DATA SCIENCE: HOW TO PREPARE FOR BIG TECH DATA SCIENCE INTERVIEWS
Big tech interviews don’t have to be intimidating if you know how to prepare properly. With so many big tech companies focusing more on cloud technologies, many data scientists are targeting the likes of Meta, Amazon, Netflix, and Google for their first jobs in the industry. While candidates will understandably have some apprehension around their interview with such companies, they are not that different from other interviews. Most big tech companies have similar interviewing practices. They are simply more selective than others which results in high rejection rates. Consequently, you don't need to worry about ‘impossible’ questions when preparing for these interviews. Instead, you should concentrate on the typical technical interview questions, paying close attention to how you will differentiate yourself from the hundreds of other highly competent applicants for the same profession. Get ahead of your interviews with a personal strategy that helps you play to your strengths and avoid your weaknesses by having a thorough understanding of how a company organises, plans, and evaluates interviews. Towards Data Science shares the following tips to remember during the interview process with big tech companies: Learn the realities and competitive landscapeData science skills are nothing without real-world problem-solving skillsYour competitors will probably have several years of industry experience and the educational qualifications to matchIf you’re making a career change, the hardest part of the process will be getting a foot in the doorChoose your learning resourcesDetermine how you will stand out during the interview process To read more about this, click here.
LABIO TECH: WHY BIOSTATISTIONS ARE ESSENTIAL FOR SUCCESSFUL CLINICAL TRIAL MANAGEMENT
Biostatisticians’ responsibilities go beyond simply analysing data at the end of a clinical study – they’re involved in the management of the clinical trial from day one in order to maximise the possibility of new treatments being authorised for the market. Overseeing these clinical trials means they have a long to-do list. They make recommendations for trial design, choose the right sample size, and ensure that the patients who are enrolled are randomised fairly. They provide definitions for data analysis, help define endpoints, and create tables and graphics for the clinical study report. “People often think that biostatistics comes in at the end of a clinical trial, but this can lead to a lot of issues, for example, when you find out too late about missing data or incorrect randomization,” said Malin Schollin, Director of Biostatistics at LINK Medical, a Swedish contract research organisation. “There is great value in having a statistician on board during the entire project because then we can take part in the decision making, and help assess how it will affect analyses, evaluation, or results.” To read more about this, click here.
ANALYTICS INSIGHT: TOP FIVE PYTHON DATA SCIENCE MINI PROJECT
Data Science uses Python to deal with massive amounts of data every single day. Many students are interested in data science and, in the same vein, work on a variety of mini projects based on data science using Python. Data science is a discipline that assists us in extracting knowledge and information from many sorts of structured or unstructured data. Here are five python Data science mini projects to explore. Real-time audio analysis – This will pique the curiosity of music enthusiasts by allowing you to perform real-time audio analysis with the Fast Fourier Transform tool, which is a crucial skill set for a data scientist. Color Detector – Determine all colour hues from a given image or video, whether it is black and white or colour. This can be quite useful for investigating officials and in the industry! Banking fraud detections – Detect credit card fraud utilising data science principles such as decision trees, neural networks, and logistic regression. Real-time image animation – Deal with visual expression dependent on camera position. This involves the use of data science in conjunction with computer vision. Business Advisor software using data science – One of the most intriguing projects because it employs exploratory data analysis, in which the programme automatically analyses the data, raises questions, and then displays facts and solutions in the form of visual graphs and other charts. To read more about this, click here.
TOWARDS DATA SCIENCE: WOULD YOU LIKE TO BECOME A BETTER DATA SCIENTIST?
START WRITING ARTICLES Data is all around us; we all generate massive amounts of data every day. Yet non-technical individuals have no idea what data is or why it is so valuable. That is why, in every presentation, a data scientist must educate their audience. You may be required to describe the data, explain how you intend to utilise it to construct a model, and present your findings. You can’t do this without critical skills in writing and communication. Businesses have a tonne of data and they need data scientists who can share their insights with others, while also understanding and using this data. Sometimes people with technical expertise aren't the best communicators, which is where strong writing skills can be especially useful. Writing can help you transmit ideas effectively. Once you become good at writing, explaining your ideas in a presentation or meeting will become more natural and fluent. When writing an article, you become more aware of your work. You can spot weak points and discover sections that need more research or could be a new topic for another article. So, believe it or not, writing can help you improve your communication skills and learn more about the field of data science. To read more about this, click here. We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

CAN’T FIND THE RIGHT OPPORTUNITY?
STILL LOOKING?
If you can’t see what you’re looking for right now, send us your CV anyway – we’re always getting fresh new roles through the door.