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Senior Data Sci­ent­ist (m/f/d)

Data Science
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What do we expect from you?

  • Bachelor’s degree in com­puter sci­ence, Data Ana­lyt­ics, Math­em­at­ics, Inform­a­tion Man­age­ment, or Stat­ist­ics. A master’s degree is pre­ferred with a focus on Data Sci­ence, Machine Learn­ing or a related discipline.
  • Min­imum of 8 years in data sci­ence, ana­lyt­ics, or a related tech­nical field, with hands-on exper­i­ence in machine learn­ing and data modeling.
  • Demon­strated suc­cess in devel­op­ing and deploy­ing data sci­ence pro­jects in large organ­iz­a­tions or com­plex environments.
  • Pro­gram­ming Lan­guages: Pro­fi­ciency in Python, R, or sim­ilar lan­guages for data ana­lysis and model development.
  • Data Mod­el­ing: Strong skills in data mod­el­ing with exper­i­ence in SQL and NoSQL databases.
  • Machine Learn­ing Frame­works: Expert­ise in frame­works such as Tensor­Flow, PyT­orch, Scikit-learn, or similar.
  • Data Sci­ence Plat­forms: Exper­i­ence with Dataiku Data Sci­ence plat­form and famili­ar­ity with Microsoft Azure ser­vices, includ­ing Azure ML Stu­dio and Azure Copi­lot Studio.
  • Data Visu­al­iz­a­tion: Pro­fi­ciency in data visu­al­iz­a­tion tools like Power BI, Tableau, or sim­ilar platforms
  • Massive Par­al­lel Pro­cessing (MPP) Tech­no­lo­gies: Famili­ar­ity with MPP plat­forms (e.g., Dat­ab­ricks, Snow­flake) is a plus.
  • Ver­sion Con­trol: Exper­i­ence with Microsoft DevOps as ver­sion con­trol systems.
  • Data Sci­ence Cer­ti­fic­a­tion from Microsoft and Dataiku (pre­ferred). Addi­tional cer­ti­fic­a­tions in machine learn­ing or data ana­lyt­ics are a plus.

Your tasks

  • Data Ana­lysis: Per­form com­pre­hens­ive ana­lysis of struc­tured and unstruc­tured data­sets using stat­ist­ical and explor­at­ory data ana­lysis (EDA) tech­niques to uncover pat­terns and insights.
  • Data Clean­ing and Trans­form­a­tion: Clean, pre­pro­cess, and trans­form raw data to ensure qual­ity and read­i­ness for ana­lysis and modeling.
  • Fea­ture Engin­eer­ing: Develop and select rel­ev­ant fea­tures to enhance model per­form­ance and pre­dict­ive capabilities.
  • Machine Learn­ing: Design, imple­ment, and val­id­ate machine learn­ing and deep learn­ing mod­els using frame­works such as Tensor­Flow, PyT­orch, or similar.
  • Pre­dict­ive Mod­el­ing: Cre­ate and refine pre­dict­ive mod­els to solve busi­ness prob­lems, lever­aging tech­niques like regres­sion, clas­si­fic­a­tion, clus­ter­ing, and time-series analysis.
  • Scal­able Pipelines: Develop and main­tain scal­able data pipelines using SQL, NoSQL, and other rel­ev­ant tech­no­lo­gies to handle large data­sets efficiently.
  • Model Deploy­ment: Deploy mod­els into pro­duc­tion envir­on­ments using plat­forms like Dataiku, ensur­ing seam­less integ­ra­tion and per­form­ance monitoring.
  • Stat­ist­ical Ana­lysis: Apply advanced stat­ist­ical meth­ods to inter­pret com­plex data sets and derive mean­ing­ful insights.
  • Data Visu­al­iz­a­tion: Cre­ate com­pel­ling visu­al­iz­a­tions and dash­boards using Power BI to effect­ively com­mu­nic­ate find­ings to tech­nical and non-tech­nical stakeholders.
  • Data Storytelling: Trans­late ana­lyt­ical res­ults into clear, action­able nar­rat­ives that sup­port stra­tegic decision-making.
  • Cross-Func­tional Col­lab­or­a­tion: Work closely with product own­ers and other data pro­fes­sion­als to develop ana­lyt­ical data products.
  • Tech­nical Sup­port: Provide sup­port to developed products.
  • Tech­nical Doc­u­ment­a­tion: Main­tain thor­ough doc­u­ment­a­tion of data pro­cesses, mod­els, and meth­od­o­lo­gies to ensure repro­du­cib­il­ity and know­ledge sharing.

What you can expect

  • Work with lead­ing edge tech­no­lo­gies that will enable you to accel­er­ate your career development.
  • Enjoy an excel­lent work envir­on­ment where people love what they do.
  • Be part of an inter­na­tional and ambi­tious team whilst hav­ing fun.
Our team

This application is processed by

Erica Matamoros Erica Matamoros
Erica Matamoros

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