SMX
Lead Data Scientist (5421)
Fort Washington, MD
No sponsorship$103k-$172kDetected 5 days ago
PythonData StructuresSQLMachine LearningData AnalysisData ScienceData VisualizationLLMsLeadership
About the role
- The leadership team will foster the organizational culture of high performing solution delivery.
- This position is onsite in Camp Springs, Maryland and requires a Top-Secret clearance.
- From priority national security initiatives for the DoD to highly assured and compliant solutions for healthcare, we understand that digital transformation is key to your future success.
Responsibilities
- SMX does not sponsor a new applicant for employment authorization or immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
Requirements
- Required Skills & Experience
- Bachelor's degree required
- Familiarity with SQL, Python/R, and visualization tools like Tableau
- Ability to communicate complex technical details clearly to diverse audiences
Compensation
- $103,100 - $171,800 USD
Benefits
- At SMX, one of our Core Values is to Invest in Our People so we offer a competitive mix of compensation, learning & development opportunities, and benefits.
- Some key components of our robust benefits include health insurance, paid leave, and retirement.
- We share your vision for the future and strive to accelerate your impact on the world.
- Selected applicant may be subject to a background investigation and/or education verification.
Equal opportunity
- SMX is an Equal Opportunity employer including disabilities and veterans.
Visa & Work Authorization
- SMX does not sponsor a new applicant for employment authorization or immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization t
This listing is sourced directly from SMX's careers page and normalized into a canonical job model.