Data Science Consultant
3 weeks ago
Company Description**
Sia Partners** is a next-generation consulting firm. We offer a unique blend of AI and design capabilities, augmenting traditional consulting to deliver superior value to our clients.
We are a global firm that has grown steadily over the past 20 years
- 2,400 Consultants
- 38 Offices across 18 countries
We invest heavily in tech and design to stay on cutting-edge and meet our clients’ evolving challenges
- 6 AI Centers
- 2 Design Centers
- 600 Clients / 92% returning
We cultivate expertise stemming from our R&D activities and proximity with our client’s industries
- 4 % of our revenue invested in R&D
- 100K Followers on LinkedIn
As the pioneer of Consulting 4.0, we leverage AI, emerging tech, and open innovation.
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Tech Lab**
We stay one step ahead of the emerging tech value offer
- **
Disruptive technologies
**RPA | IoT | 5G | Blockchain | DevOps | Quantum Computing
- **
Advanced AI Technology**
Voice Recognition/Virtual Assistants | Voice recognition | Satellite image recognition | Multilingual sentiment analysis | Time -Series | Machine Learning on edge
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Tech Greenfields**
Complex ecosystem transcending conventional business
BIM | Autonomous Vehicles | Connected Home | Predictive healthcare | Digital currencies
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AI Centers**
We boost the transformative impact of AI through our solutions
- Heka, our AI ecosystem:
- PAAS
- AI solutions
- AI accelerator (Algorithms/models/APIs POC & data sets)
- Production and industrialization of AI projects
**Open Innovation**
We develop innovative ecosystems, both independently and with our clients
- STUDIO, our startup investment arm
- APIFICATOR, international challenge for tech B2B startups
- Alliances and partnerships bringing added value to our clients
- Helping our clients develop their own innovative ecosystems
**Job Description**:
To support, Sia Partners’ development in the Middle East, Sia Partners is recruiting data scientist consultants.
Their vocation will be to take charge, in synergy with our different practices, of the work and missions requiring the implementation of complex models to support our clients address their business challenges related to:
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Machine learning and statistics**: Development of explanatory and predictive decision-support models (directed and non-directed learning algorithms, econometrics, forecasting, quantitative diagnosis, etc.).
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Operations research**: assistance in the design and implementation of optimization solutions under constraints and simulation models.
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Big Data**: algorithmic knowledge for processing large volumes of unstructured data.
**As part of the firm's internal activities, your participation revolves around the following areas**:
- The development or strengthening of our offers through training, working groups, dissemination of internal and external support.
- The publication policy (sector blogs, studies, press release, etc.).
- Commercial development by defining needs and participating in commercial actions
Qualifications**
- You have a training in Engineering School or a high-level training in the field of Data Science, statistical engineering, and econometrics, and you ideally justify a first experience in Big Data or Data Science (internship or 1st job).
High capabilities in framing machine learning problems.
- Strong business acumen, as well as the ability to communicate findings, and mine vast amounts of data for useful insights.
- Proficiency in at least 2 of the following: Python, R, SQL.
- Solid foundation in scripting languages like (Python, Perl, JS, bash) or compiled (C ++, Java) and/or have experience in environments UNIX / LINUX.
- You want to get involved and quickly take responsibility for high added value missions within a dynamic structure.
- You have the ability to work in a team, an open mind, a sense of analysis and you want to join a motivating professional environment where you share our values of excellence, entrepreneurship, innovation, the culture of sharing, benevolence, personal / professional life balance.
**and possess Expertise in some or all of the following**:
- Data cleaning, manipulation and pre-processing, exploratory analysis, feature engineering and machine learning libraries like pandas, numpy, scipy, regex, matplotlib, seaborn, statsmodels, scikit-learn, xgboost, and nltk, genism or spacy.Supervised Learning: Regression (simple, multivariate) and Classification (binary, multi-class, multi-label).
- Unsupervised Learning: Clustering (customer segmentation, cluster and community detection) and Dimensionality Reduction (PCA, LDA, TruncatedSVD, NNMF, etc.).
- Recommender Systems: Collaborative Filtering, Content Based and Matrix Factorization Methods.
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Natural Language Processing: Text Preprocessing (Arabic & English), Word2Vec, TF-IDF, Text Classification (binary, multi-class, muli-
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