Demand for data science professionals is seeing a growing uptake.
Job opportunities in data science continue growing as companies are generating massive amounts of data and seek to excerpt valuable insights from them. The field of data science could be a key in traditional industry dynamics, as organizations can have access to huge information to make data-driven strategic decisions that keep them ahead of their peers. Data science roles, including data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, and more are in high demand from big tech giants this month.
As these tech companies look to optimize their complex processes and gain a competitive advantage from the data they collect, they are investing huge capital in data science skills paying handsome salaries to data professionals.
Here’s a look at the top data science jobs in October 2020.
Data Scientist – Ericsson-Worldwide
Ericsson is a provider and operator of telecommunication networks, television and video systems, and related services. Candidates for this position at the company will be responsible for developing scientific methods, processes, and systems to extract knowledge or insights to drive the future of applied analytics. They will mine and assess data from company databases to drive optimization and improvement of product development and business strategies. A candidate will also require analyzing the effectiveness of new data sources and data gathering techniques, developing custom data models and algorithms to apply to data sets. Further, they will use predictive modeling to enhance customer experiences, revenue generation and other business outcomes and more.
• Solid understanding in Statistics, e.g., hypothesis formulation, hypothesis testing, descriptive analysis, and data exploration.
• Aptitude and skills in Machine Learning, e.g., linear/logistics regression discriminant analysis, bagging, random forest, Bayesian model, SVM, neural networks, etc.
• Strong programming skills in various languages (Python, Scala, R)
• Familiarity with Linux/OS X command line, version control software (git), and general software development.
• Experience in programming or scripting to enable ETL development
• Familiarity with relational databases.
Digital Data Engineering Practitioner – Accenture
Accenture is a leading global professional services company, providing a broad range of services in strategy and consulting, interactive, technology and operations, with digital capabilities across all of these services. In this role, an aspirant will be responsible to develop analytics-based solutions that produce quantitative and qualitative business insights. They will require working with partners as necessary to integrate systems and data quickly and effectively, regardless of technical challenges or business environments.
• Exp in Data science frameworks Jupyter notebook, AWS Sagemaker, etc
• Exp querying databases and using statistical computer languages: R, Python, SLQ, etc
• Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis,
• Exp with distributed data/computing tools: Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL
• Experience using cloud services: RDS, Athena, Redshift, Kinesis, S3, AWS glue
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks
• Exp in visualizing/presenting data for stakeholders using: AWS Quicksight, Tableau, Periscope, Business Objects, D3, ggplot
Business Analyst – Data Science – Genpact
For the role of Business Analyst and Data Science at Genpact, candidates will be responsible to demonstrate innovation and intuition in identifying areas requiring operational adaptation and/or improvement of active users on the Internet and online applications. Along with this, they are also required to execute deliverables by Business Intelligence and Analysis, Designing, Testing, Migration, Production Support and Implement automation. They will also be responsible for interpreting data, evaluate results using statistical techniques and provide ongoing reports. They need to develop and implement databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality.
• Knowledge of Visualization tool like Power BI / Tableau
• Knowledge of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, etc., and their pros and cons. Proficiency in Big data handling tools such as Python/R programming knowledge
• Must possess problem-solving skills and be proactive in researching solutions.
• Advanced Excel skills (i.e. complex formulas)
• Ability to programmatically manipulate worksheet and cell properties using VBA.
Data Science Analyst – Thermo Fisher Scientific
The Data Science Analyst will be a key member of the Corporate Digital Marketing team at Thermo Fisher India. In this role, a candidate will help identify and shape the company’s strategy on how it will evaluate its current businesses, which new areas it should start looking at and how does it achieve its broader 10-year vision – all of these augmented by the power of data and analytics. The candidates will be responsible for assessing revenue/financial data; market research data to identify actionable trends. They will work with cross-functional teams like Sales, Finance and IT to understand data and analytics related requirements and define potential projects. Moreover, they have to ensure that the assigned deliverables are completed end-to-end, and work with sometimes ambiguous requirements across functions in a matrix structure.
• Advance knowledge of Microsoft Excel
• Experience in SQL, Python or R
• Experience with visualization tools like Power BI or Tableau
Data Science Consultant – Hewlett Packard Enterprise
The role of Data Science Consultant at HPE will span around designing, developing and applying programs, methodologies and systems based on advanced analytical models (e.g. advanced statistics, operations research, computer science, process). Further they’ve to transform structured and unstructured data into meaningful and actionable data insights that drive decision making. A candidate for this position will require using visualization techniques to translate analytical insights into understandable business stories (eg. descriptive, inferential and predictive insights). They will have to embed analytics into clients’ business processes and applications, and combine business acumen and scientific methods to solve business problems, and more.
• Advanced knowledge of advanced data science methodologies including but not limited to classical regression, neural nets, CHAID, CART, association rules, sequence analysis, cluster analysis, and text mining.
• Advanced understanding of analytics software (eg. R, SAS, SPSS, Python). Advanced understanding of analytics deployment architectures.
• Advanced machine learning, data integration, and mathematical modeling skills and ETL tools (eg. Informatica, Ab Initio, Talend).
• Advanced communication and presentation skills.
Data Analyst – Condeco
For this role at Condeco, a candidate will be responsible for identifying and assessing key opportunity areas where data, analytics and science can enhance the company’s business. They will require developing trends and key drivers in consumer behaviours that can be used to develop compelling value-props for the company and its clients. They also need to drive productization of analytic capabilities in data management, modelling and insights. They will glean information, perform data analysis by using the latest data technologies to define technology requirements, business rules and ensure the technology meets the business objectives.
• Expertise in Power BI visualization
• Should have worked as Business/Data Analyst for a Data Lake or Warehousing project, preferably on Big Data platform
• Strong SQL skills are necessary along with exposure to data analysis using industry-standard tools, methods and techniques. Prior experience of data modelling is useful.
• Ability to analyse existing tools and databases and provide software solution recommendations.
Hadoop OR Python OR kafka OR spark